The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo

The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo Milagrosa López-Riego, Magdalena Płódowska, Milena Lis-Zajęcka, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2873007/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jun, 2023 Read the published version in Radiation and Environmental Biophysics → Version 1 posted 7 You are reading this latest preprint version Abstract Candidate ionising radiation exposure biomarkers must be validated in humans exposed in vivo . Blood from patients undergoing positron emission tomography - computed tomography scan (PET-CT) and skeletal scintigraphy (scintigraphy) was drawn before (0 h) and after (2 h) the procedure for correlation analyses of response of selected biomarkers with radiation dose and other available patient information. FDXR , CDKN1A , BBC3 , GADD45A , XPC and MDM2 expression was determined by qRT-PCR, DNA damage (γH2AX) by flow cytometry, and reactive oxygen species (ROS) levels by flow cytometry using the 2', 7' – Dichlorofluorescin diacetate test in peripheral blood mononuclear cells (PBMC). For ROS experiments, 0- and 2 h samples were additionally exposed to UVA to determine whether diagnostic irradiation conditioned the response to further oxidative insult. With some exceptions, radiological imaging induced weak γH2AX foci, ROS and gene expression fold changes, the latter with good coherence across genes within a patient. Diagnostic imaging did not influence oxidative stress in PBMC successively exposed to UVA. Correlation analyses with patient characteristics led to low correlation coefficient values. γH2AX fold change, which correlated positively with gene expression, presented a weak positive correlation with injected activity, indicating a radiation-induced subtle increase in DNA damage and subsequent activation of the DNA damage response pathway. The exposure discrimination potential of these biomarkers in the absence of control samples, as frequently demanded in radiological emergencies, was assessed using raw data. These results suggest that the variability of the response in heterogeneous populations might complicate identifying individuals exposed to low radiation doses. gene expression γH2AX foci ROS lymphocytes blood diagnostic imaging patients. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Following genotoxic stress induced by the direct action of ionising radiation and, indirectly, by reactive oxygen species (ROS), the DNA damage response (DDR) is activated to preserve the integrity of the genome. Oxidative stress from water radiolysis is further amplified by ROS-producing cellular systems such as mitochondria (Reisz et al. 2014 ; Szumiel 2015 ). Early on in the DDR cascade, serine 139 of histone H2AX (γH2AX) is phosphorylated, which signals the presence of DNA double strand breaks (DSBs) (Rogakou et al. 1998 ), one of the most deleterious DNA lesions (Schipler and Iliakis 2013 ). Downstream, a complex network of pro-survival or pro-death genes, usually p53-controlled (Hu et al. 2022 ), interact to determine the cellular fate (Christmann and Kaina 2013 ; Roos et al. 2016 ) both after environmentally-relevant (Amundson et al. 2000 ; Sokolov and Neumann 2015 ) and high doses (Beer et al. 2014 ; El-Saghire et al. 2013 ). Exploiting these molecular changes and the cytogenetic end-products that may follow ionising radiation (IR) exposure, a range of IR biomarkers have been proposed for use in biodosimetry (Swartz et al. 2010 ) or epidemiological studies (Hall et al. 2017 ; Pernot et al. 2012 ). The validation of these IR biomarkers requires appropriate human models. Numerous validation efforts have been published for the γH2AX assay (Ainsbury et al. 2014 ; Barnard et al. 2015 ; Rothkamm et al. 2013 ) and gene expression (Abend et al. 2021 ; Abend et al. 2023 ; Abend et al. 2016 ; Badie et al. 2013 ; Biolatti et al. 2021 ; Manning et al. 2017 ) as IR biomarkers sensitive to doses in the mGy range (Schule et al. 2023 ). γH2AX was tested as a biomarker of DNA damage and repair in human population studies related to diagnostic procedures (Brand et al. 2012 ; Halm et al. 2014 ; Kuefner et al. 2009 ; Lobrich et al. 2005 ; Pathe et al. 2011 ; Rothkamm et al. 2007 ; Vandevoorde et al. 2015 ), chemotherapy (Halicka et al. 2009 ; Karp et al. 2007 ; Sak et al. 2009 ) and/or radiotherapy (Sak et al. 2007 ; Zahnreich et al. 2015 ; Zwicker et al. 2011 ), as previously reviewed (Valdiglesias et al. 2013 ). The application of gene expression profiles in epidemiological studies has so far been limited by their transient nature (Hall et al. 2017 ). Nevertheless, transcription studies provide a valuable source of information for our understanding of cellular response to low doses (Sokolov and Neumann 2015 ), a dose range where stochastic effects are poorly defined due to larger uncertainties of epidemiological data (Kreuzer et al. 2018 ). Being able to reflect radiation exposure over a wide range of doses (Amundson et al. 2001 ; Amundson and Fornace 2001 , 2003 ; Manning et al. 2013 ), transcriptomic biomarkers can also support dose reconstruction (Ghandhi et al. 2019a ), triage (Port et al. 2019 ) and clinical outcome prediction (Port et al. 2016 ) in the event of radiological emergency. Fast readout, within hours to days, the possibility of high-throughput analysis (Ostheim et al. 2022 ), ease of sampling and high sensitivity early after exposure (Paul et al. 2011 ) add substantial value to their use in biodosimetry. In addition, the development of a transcriptomic dosimeter could help in estimating internal doses in radionuclide therapy and internal contamination, which currently relies on whole body counting, biokinetic models as well as bioassays on urine or faecal samples (Edmondson et al. 2016 ). Due to ethical considerations and given the restricted availability of suitable human samples, transcriptomic IR biomarker characterization usually entail mice (Ghandhi et al. 2019b ), non-human primate models (Park et al. 2017 ; Port et al. 2018 ), and, to a large degree, human ex vivo exposed samples (Abend et al. 2021 ; Abend et al. 2016 ; Badie et al. 2013 ; Cruz-Garcia et al. 2018 ; Kaatsch et al. 2021 ; Kaatsch et al. 2020 ; Kabacik et al. 2011 ; Knops et al. 2012 ; Manning et al. 2017 ; Nosel et al. 2013 ; Paul and Amundson 2011 ; Schule et al. 2023 ). Although these are relevant models to the human in vivo response (Lucas et al. 2014 ; O'Brien et al. 2018 ; Paul et al. 2011 ) and help to understand the potential impact of cofounding factors, e.g. inflammation (Mukherjee et al. 2019 ) or DNA repair capacity (Rudqvist et al. 2018 ), the problems of interspecies differences (Lucas et al. 2014 ; Satyamitra et al. 2022 ), blood cell deterioration, the absence of tissue signalling (Ghandhi et al. 2019a ) and cellular microenvironment (Filiano et al. 2011 ) in culture are acknowledged. Consequently, candidate IR transcriptomic biomarkers must be validated in humans exposed in vivo (Paul et al. 2011 ). The number of gene expression studies using in vivo -IR exposed human blood samples from either occupational (Fachin et al. 2009 ; Morandi et al. 2009 ; Sakamoto-Hojo et al. 2003 ), environmental (of natural or accidental origin) (Albanese et al. 2007 ; Jain and Das 2017 ), diagnostic or therapeutic exposures (Abend et al. 2016 ; Amundson et al. 2004 ; Campbell et al. 2017 ; Cruz-Garcia et al. 2022 ; Cruz-Garcia et al. 2018 ; Cruz-Garcia et al. 2020 ; Dressman et al. 2007 ; Edmondson et al. 2016 ; Evans et al. 2022 ; Filiano et al. 2011 ; Lucas et al. 2014 ; Meadows et al. 2008 ; O'Brien et al. 2018 ; Paul et al. 2011 ; Port et al. 2018 ; Riecke et al. 2012 ) is limited, but the available results clearly demonstrate the usefulness of transcript signatures as biomarkers of radiation exposure. Differential gene expression profiles are detected in human peripheral blood mononuclear cells (PBMCs) isolated from X- and γ-radiation-exposed health care workers exposed to doses < 25 mSv (Morandi et al. 2009 ). Also, an overrepresentation of DDR and p53-related genes is found among differentially expressed genes (DEGs) in PBMC of individuals living in high natural background radiation areas (Jain and Das 2017 ). For instance, CDKN1A is 1.5 fold upregulated in individuals exposed to > 15 mGy/year and MDM2 is 1.5-fold upregulated in those exposed to 5–15 mGy/year (Jain and Das 2017 ). Differential expression of p53 target genes is also observed in samples from patients exposed, primarily, to external radiation (Abend et al. 2016 ; Amundson et al. 2004 ; Cruz-Garcia et al. 2022 ; Cruz-Garcia et al. 2018 ; Cruz-Garcia et al. 2020 ; Dressman et al. 2007 ; Filiano et al. 2011 ; Lucas et al. 2014 ; Meadows et al. 2008 ; O'Brien et al. 2018 ; Paul et al. 2011 ; Port et al. 2018 ; Riecke et al. 2012 ), with some exceptions (Campbell et al. 2017 ; Edmondson et al. 2016 ; Evans et al. 2022 ; Lee et al. 2015 ). Fold changes of ca 16, 7, and 3 are observed on average for FDXR, CDKN1A , and BBC3 , respectively, in patients exposed to 1.25 Gy total body irradiation (TBI) (Paul et al. 2011 ). FDXR upregulation was shown to increase with dose from diagnostic CT scans to TBI and radiotherapy (O'Brien et al. 2018 ). In this study we sought to correlate the radiation dose with the expression of FDXR , CDKN1A , BBC3 , GADD45A , XPC and MDM2 along with levels of γH2AX and ROS in PBMC of patients undergoing positron emission tomography - computed tomography scan (PET-CT) and skeletal scintigraphy (scintigraphy). Blood was collected before and 2 h after the diagnostic intervention so that the individual background and radiation-induced signal levels could be compared. Additionally, correlation analyses with available individual information such as age, sex, or records of previous radiotherapy or chemotherapy treatment were performed to assess the impact of these factors on the studied biomarkers of exposure. Materials and methods Donor information and blood sample collection Blood samples were obtained at the Department of Nuclear Medicine with Positron Emission Tomography Unit of the Holy Cross Cancer Centre in Kielce (Poland) from patients undergoing diagnostic PET-CT (n = 17) and scintigraphy (n = 17). Sampling was carried out randomly during the period of March-June 2022 mostly on Monday and Tuesday, in the morning hours. On one day, between two and three patients were sampled that underwent the diagnostic procedure consecutively. Blood was collected by venipuncture in EDTA tubes (BD Vacutainer) and into PAXgene® Blood RNA tubes (BD Biosciences) before (0 h) and after (2 h) the diagnostic procedure. Each blood sample was coded with a letter corresponding to the procedure (P for PET-CT and S for skeleton scintigraphy), a number ranging from 1 to 17 and the blood collection time point (0 and 2). Blood was collected separately for gene expression analysis and the other endpoints. For gene analysis and activity measurements 2.5 ml blood was directly collected into PAXgene® Blood RNA tubes (see below for details), for the other endpoints – into EDTA tubes. Samples were stored at room temperature and were transported to the Jan Kochanowski University (Kielce) between 30 and 60 min after the 2 h blood sampling. Transport to the university took 15–20 min. After arrival radioactivity was measured in the PAXgene® tubes at room temperature. The tubes were then gradually frozen according to guidelines of the manufacturer (-20°C followed by -80°C), shipped in two batches on dry ice to Stockholm University (September 2022 and December 2022) and stored there at -80°C until further processing. EDTA samples were processed for the endpoints described below within 60 min of their arrival. For analysis of γH2AX foci and ROS, peripheral blood mononuclear cells (PBMC) from ca 5 ml of each blood sample were isolated by gradient centrifugation. To this end blood was diluted 1:1 in phosphate buffered saline (PBS) and overlaid on LymphoprepTM (Serumwerk Bernburg AG for Alere Technologies AS, Oslo, Norway) and centrifuged at 400 x g for 30 min. The layer containing lymphocytes was removed and washed three times with phosphate-buffered saline (PBS). Cells were counted and 1/3 was used for analysis of γH2AX and 2/3 for analysis of ROS as described in detail below. The general experimental setup is graphically shown in Fig. 1 . Available information regarding the individuals included in this study such as sex, age, body mass or records of previous radiotherapy (RT) or chemotherapy (CHT) is provided in Table 1 . The cohort included 29 males and 5 females, with ages ranging from 41 to 80, with an average age of 66. For scintigraphy imaging, the 99m Tc- methylene diphosphonate ( 99m Tc-MDP) was used. PET patients were diagnosed with squamous cell carcinoma (SCC), melanoma, sarcoma, thymus cancer, head and neck cancer (HNC), lung cancer, bronchus cancer, neoplasm of uncertain behaviour of trachea, bronchus and lung (D38.1), Hodgkin lymphoma, pancreas cancer, prostate cancer or malignant neoplasm of endocrine gland (C75.9). They received either 18 F-Fluorodeoxyglucose ( 18 F-FDG), [ 18 F]-labeled prostate-specific membrane antigen ( 18 F-PSMA) or 68 Ga-DOTA-Phe1 Tyr3-octreotate ( 68 Ga-DOTATATE). The study was approved by the ethical committee of the Regional Medical Chamber in Kielce. Gene expression analysis RNA extraction was performed using the PAXgene Blood RNA Kit (PreAnalytiX), following the manufacturer´s instructions. cDNA was synthesised using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) from 95 ng RNA, to maximise the RNA input in the reaction based on the lowest RNA concentration sample. For real time PCR, duplicate reactions of primers, cDNA and 5x HOT FIREPol® EvaGreen® qPCR Supermix (Solis BioDyne) were setup and run on a LightCycler® 480. The cycling conditions were: 95°C (15 min), 40 cycles of 95°C (15 s), 60°C (20 s) and 72°C (20 s). The 2 −ΔΔCt method was used for calculation of relative expression, using the housekeeping 18S gene for normalisation. Primer specificity was confirmed using melting curve analysis. Forward (for) and reverse (rev) primers (5´- 3´) used were described earlier for genes of interest (Cheng et al. 2018 ) and housekeeping 18S (Lundholm et al. 2014 ). These were: GADD45a_ for (actgcgtgctggtgacgaat), GADD45a_ rev (gttgacttaaggcaggatccttcca), BBC3 _for (tacgagcggcggagacaaga), BBC3 _rev (gcaggagtcccatgatgagattgtac), MDM2 _for (tatcaggcaggggagagtgataca), MDM2 _rev (ccaacatctgttgcaatgtgatggaa), XPC _for (gcttggagaagtaccctacaagatggt), XPC _rev (ggctttccgagcacggttaga), FDXR _for (tggatgtgccaggcctctac), FDXR _rev (tgaggaagctgtcagtcatggtt), CDKN1A _for (cctggagactctcagggtcgaaa), CDKN1A _rev (gcgtttggagtggtagaaatctgtca), 18S _for (gcttaatttgactcaacacggga), 18S _rev (agctatcaatctgtcaatcctgtcc). Gene expression results here represent an average of the response of all leukocytes, including peripheral blood lymphocytes and granulocytes, lysed in the PAXGene system. γH2AX analysis Isolated PBMC were washed once in PBS and fixed for 10 minutes in Cytofix Fixation Buffer (Becton Dickinson, cat. no. 554655). Cells were washed again in PBS, 90% methanol (Chempur, Poland, chilled at -20 o C) which was added drop by drop and left for permeabilization for 5 minutes. Cells were washed in Perm/Wash Buffer (Becton Dickinson, cat. no. 554723), incubated with Alexa Fluor 647 Mouse anti-H2AX pS139 (Becton Dickinson, cat. no. 560447) for 60 minutes and washed with Perm/Wash. Cells were resuspended in 300 µl Stain Buffer (FBS, Becton Dickinson, cat. no. 554656) and the level of γ-H2AX fluorescence was measured with a LSR II flow cytometer (Becton Dickinson USA). Alexa Fluor 647 was excited by the red laser (627–640 nm) and detected using an optical filter centered near 520-nm (e.g., a 660/20 nm bandpass filter). The BD FACS DiVa (version 6.0, Becton Dickinson) was used for data acquisition and analysis. 20,000 events were stored. Per sampling time and patient, the median focus intensity was calculated and used for analyses. ROS analysis Oxidative stress induced by UVA was quantified with the help of the 2', 7' – Dichlorofluorescin diacetate (DCFDA) test (Sigma Aldrich, D6883). PBMC incubated for 15 min in the stain buffer at 37 ○ C), then DCF was added for 30 min. Cells were split into two Petri dishes. One was irradiated with UVA on ice (see below) and the other was sham exposed. Next, the cells were transferred to cytometer tubes and the level of fluorescence was measured with a LSR II flow cytometer (Becton Dickinson, USA). A computer system BD FACS DiVa (version 6.0, Becton Dickinson) was used for data acquisition and analysis. Data for 20,000 events were stored. Per sampling time and patient, the median signal intensity was calculated and used for analyses. UV irradiation was carried out with a 2G11 55 W DULUX L BL lamp, OSRAM, Germany, operating in the UVA range (315–400 nm). The irradiation time was 20 min and the UV dose was 0.3 kJ/cm 2 . Dosimetry was carried out with a CHY 732 320–400 nm UVA meter, CHY FIREMATE Co., LTD, UK. The dose was selected based on unpublished results from student projects where it was found to induce a strong signal. Activity measurements and effective dose calculations The activity of blood in each PAXgene® tube was measured using a nitrogen cooled high purity germanium (HPGe) detector (model GX3020 - b12075) placed in a shielded container and connected to a Genie™ 2000 Spectroscopy Software, Canberra Industries, Inc, USA. Prior to measuring the activity of blood samples, the spectrometer was pre-calibrated with calibration sources containing 99m Tc and 18 F isotopes of known activity. The activity of the blood samples was measured approximately 2–4 h after sampling 2 h samples and converted to the sampling time based on the half-life of the specific radioisotope. The injected radionuclide activities were documented for each patient and converted to effective doses using the isotope-dependent conversion factors (mSv/mBq): 0.0066 for 99m Tc-MDP (Batista da Silva et al. Congresso Brasiliano de Metrologia das Radiacoes Ionizantes, Rio de Janeiro, 28.11.2018) 0.027 for 18 F-FDG (ICRP 1988 ), 0.022 for 18 F-PSMA (Giesel et al. 2017 ) and 0.0257 for 68 Ga-DOTATATE (Walker et al. 2013 ). Statistical analyses Results from 0 h collection times were compared to 2 h using paired t-tests or one-way ANOVA with multiple comparison corrections. Results from scintigraphy and PET patients were compared using unpaired t-tests. P-values are provided together with Cohen´s effect size d-values, in accordance with the claim that scientific conclusions should not be solely based on significance tests (Amrhein et al. 2019 ). The following criteria were applied for effect sizes: d 1.3: very large effect (Cohen 1988 )T-tests (paired and unpaired), one-way ANOVA, linear regression analyses (Y = slope*X + Y-intercept), and correlation analyses to obtain Pearson r- values were performed using GraphPad Prism 9.4.1. Detailed results of the analyses are provided in supplemental tables as specified in the text and figure legends. Table 1 Cohort information. 17 PET-CT (P) patients treated with 18 F-FDG, 18 F-PSMA and 68 Ga-DOTATATE and 17 skeletal scintigraphy (S) patients treated with 99m Tc-MDP were included in this study. Each individual was coded with a letter attending to the corresponding diagnostic procedure (P/S) and a number 1–17. Information regarding: sex, age, body mass, diagnosis, year of previous radiotherapy (RT) or chemotherapy (CHT) treatment, if applicable, and injected activity was recorded. SCC: squamous cell carcinoma. H&N: head and neck. D38.1: Neoplasm of uncertain behaviour of trachea, bronchus and lung. C75.9: Malignant neoplasm of endocrine gland, unspecified. Effective dose (E dose, mSv/mBq) calculated using the following conversion factors: 0.0066 (for 99m Tc-MDP), 0.027 ( 18 F-FDG), 0.022 ( 18 F-PSMA) and 0.0257 ( 68 Ga-DOTATATE). Activity left in blood at 2 hours post-treatment was measured with a HPGe detector in Bq and converted to mBq, corresponding to numerical values shown in the blood analysis activity column. The percent of injected activity left was calculated based on the injected activity and the activity left in blood at 2 hours Patient Sex Age Body Diagnosis RT CHT Injected E Blood analysis Percent Treatment code mass record record activity dose activity left (M/F) (Years) (Kg) (Year) (Year) (MBq) (mSv) (mBq) (%) Scintigraphy 99m Tc-MDP S-01 F 51 84 Scintigraphy 2021 2021 714 4.71 12.60 1.76 S-02 M 67 98 Scintigraphy 745 4.92 10.50 1.41 S-03 F 71 62 Scintigraphy 718 4.74 NA S-04 M 67 89 Scintigraphy 740 4.88 14.00 1.89 S-05 M 69 83 Scintigraphy 771 5.09 4.70 0.61 S-06 M 80 65 Scintigraphy 720 4.75 14.90 2.07 S-07 M 75 120 Scintigraphy 2021 746 4.92 12.00 1.61 S-08 M 62 83 Scintigraphy 729 4.81 9.44 1.29 S-09 F 43 81 Scintigraphy 2020 2021 726 4.79 16.20 2.23 S-10 M 69 85 Scintigraphy 706 4.66 12.00 1.70 S-11 M 73 110 Scintigraphy 721 4.76 12.60 1.75 S-12 M 53 100 Scintigraphy 2022 2022 738 4.87 5.79 0.78 S-13 M 75 81 Scintigraphy 755 4.98 11.70 1.55 S-14 M 67 90 Scintigraphy 718 4.74 13.30 1.85 S-15 M 68 107 Scintigraphy 2009 731 4.82 12.50 1.71 S-16 M 73 86 Scintigraphy NA NA NA S-17 M 78 NA Scintigraphy 712 4.70 10.30 1.45 PET 18 F-FDG P-01 M 72 75 Lung C, SCC 2014 252 6.80 5.60 2.22 P-02 F 76 74 Melanoma 217 5.86 6.66 3.07 P-03 F 47 57 Sarcoma 2019 2019 202 5.45 4.39 2.17 P-05 M 53 88 Thymus C 2021 2021 287 7.75 6.90 2.40 P-09 M 49 83 H&N C 2020, 2021 290 7.83 5.52 1.90 P-10 M 75 85 Lung C, SCC 284 7.67 6.93 2.44 P-11 M 74 94 Bronchus C 307 8.29 4.26 1.39 P-12 M 69 97 D38.1 313 8.45 1.06 0.34 P-13 M 41 90 Hodgkin L 2021 287 7.75 4.97 1.73 P-14 M 70 57 D38.1 193 5.21 5.64 2.92 P-15 M 48 101 Pancreas C 339 9.15 4.17 1.23 18 F-PSMA P-06 M 70 107 Prostate Ca 362 7.96 6.38 1.76 P-07 M 71 86 Prostate Ca 359 7.90 4.93 1.37 P-08 M 72 87 Prostate Ca 362 7.96 8.67 2.40 P-16 M 79 69 Prostate Ca 243 5.35 15.10 6.21 P-17 M 65 84 Prostate Ca 258 5.68 2.86 1.11 68 Ga - DOTATATE P-04 M 71 83 C75.9 155 3.98 0.26 0.17 Results PET-CT and scintigraphy induce weak fold changes in gene expression, γH2AX foci and ROS relative to unexposed samples Gene expression of a panel of six radiation-responsive genes was analysed in PBMC by qPCR before (0 h) and 2 h after PET-CT and scintigraphy. The fold change of each gene at 2 h relative to control (0 h) was calculated for each of the 17 patients per group. PBMC were also analysed for DNA damage by the γH2AX focus test by flow cytometry and for ROS levels by the DCFDA test using flow cytometry. For ROS analysis, aliquots of PBMC collected at 0 h and 2 h were exposed to UVA to determine the possible impact of PET-CT and scintigraphy on the response of cells to oxidative stress induced by a strong oxidative insult. The activity of radionuclides in the blood collected at 2 h was measured with a germanium detector (Fig. 1 ). In order to graphically visualise summarised results of all assays, a heat map was constructed where results of all assays are presented as fold changes of data from 2 h over 0 h. Patients were ranked from highest to lowest with respect to fold gene expression (Fig. 2 A). Per patient, the six analysed genes responded fairly similarly as can be judged by the relatively uniform horizontal colour patterns: orange at top rows and blue at bottom rows. No obvious relationship can be seen at this projection between gene expression, γH2AX, ROS and activity measurements. High effective dose values (black and dark grey boxes) clustered in the top 17 rows, suggesting a positive relationship with gene expression. More results presented as fold changes are shown in Fig. 2 and described in greater details below. Figure 2 B shows the individual and mean results per gene and patient group. None of the genes was significantly upregulated at 2 h, but some showed a medium effect size such as BBC3 in PET-CT patients (p = 0.41, d = 0.74) and scintigraphy patients (p = 0.75, d = 0.63), FDXR (p = 0.78, d = 0.55) and XPC (p = 0.87, d = 0.69) in PET-CT patients only. Unpaired t test was used in order to test whether the PET-CT and scintigraphy patients differed in the gene expression response. For none of the genes there was a statistically significant difference, Supplemental Table 1. Effect sizes were small, with the exception of FDXR , for which a medium effect size (d = 0.52) was detected. In an attempt to test if the diagnostic radiation exposure had an impact on overall gene expression per patient, the average fold changes of all 6 genes were calculated. The results are shown in Fig. 2 C. The mean fold change of all genes per patient was somewhat higher and more spread out in the group of PET-CT (1.20 ± 0.15) as compared to scintigraphy (1.07 ± 0.24) patients. The increase in fold change was significant (p = 0.001) and of large size (d = 1.92) in PET-CT patients but not significant (p = 0.31) and small (d = 0.43) for scintigraphy patients. The difference in gene fold change between both patient groups was not significant (p = 0.37) but medium (d = 0.65). Individual gene expression results are shown in Supplemental Fig. 1 for PET-CT and in Supplemental Fig. 2 for scintigraphy patients. Figure 2 D shows results of the γH2AX test. In contrast to gene expression, a higher level of response was detected in scintigraphy then in PET-CT patients: the fold change in the former patient group was significant (p = 0.014) and of large size (d = 1.00), while in the latter group it was not significant (p = 0.33) and of medium size (d = 0.708). Individual γH2AX test results for both patient groups are shown in Supplemental Fig. 3. The results of ROS analyses are presented in Fig. 2 E. Except for 3 PET-CT patients, ROS fold changes in samples not additionally exposed to UVA were not distinguishable from 1 meaning that the diagnostic radiation exposure did not induce detectable oxidative stress. Additional exposure of 2 h samples to UVA radiation did not induce significant ROS level changes relative to 0 h samples exposed to UVA in any of the groups, but a large effect, with a downregulation pattern, was seen in PET-CT patients (p = 0.82, d = 0.95), not in scintigraphy patients (p > 0.99, d = 0.01) with the exception of one patient with an 8-fold decrease. This indicated that the diagnostic exposure of patients did not change the response of PBMC to additional oxidative stress induced by UVA irradiation. Complete numerical values of statistical tests are given in Supplemental Table 1. In order to increase the statistical power of the analysis, fold changes from the two patient groups were pooled (Fig. 2 F). The result of gene expression and γH2AX was non-significant and of small size (except BBC3 and XPC , for which the effect sizes were medium, and γH2AX, for which the effect size was large, see Supplemental Table 1 for p and d values). Highly scattered results were obtained for ROS without UVA with 2 donors showing fold changes of around 8. As expected, based on results shown in Fig. 2 E, additional UVA exposure did not lead to significant effects (p = 0.8, d = 0.16). Moreover, with the aim of seeing whether the combination of fold changes from all endpoints improved the power to detect radiation exposure, fold changes were pooled separately for PET-CT and scintigraphy patients and for both groups of patients together. The results are shown in Fig. 2 G. Interestingly, pooling of fold changes per patient group resulted in a somewhat higher and more spread-out values in the PET-CT as compared to scintigraphy cohort. Finally, pooling of all patients resulted in a significant but small mean fold change, with 1 out of 34 patients showing a combined fold change above 2, and 9 – above 1.3. A twofold change relative to control is considered a conservative threshold which controls for false positive results (Riecke et al. 2012 ), but a fold change threshold of 1.3 is also considered biologically relevant (Jain and Das 2017 ). Correlation of gene expression, γH2AX foci and ROS changes with patient characteristics Fold changes observed for the pool of patients for each of the endpoints were correlated to injected activity (Fig. 3 ), calculated effective dose (Fig. 4 ), blood activity left in blood at 2 hours post-procedure (Fig. 5 ) and percent of injected activity left at 2 hours (Supplemental Fig. 5). In these figures, panels A-K show the corresponding correlations for: BBC3 , CDKN1A , FDXR , GADD45A , MDM2 , XPC , γH2AX, ROS (after PET or scintigraphy only or after additional UVA exposure), pool of genes and pool of endpoints, respectively. Additional correlation analyses were performed to determine how the different fold changes correlated between endpoints (Supplemental Fig. 6). Numerical values regarding linear regressions of these correlations, including equations, 95% confidence intervals, R 2 and Pearson r values are provided in Supplemental Tables 2–6. Overall, there was a lack of steep slopes with low R 2 and r values. γH2AX fold change presented a weak positive correlation with injected activity (Fig. 3 G and Supplemental Table 2). Conversely, ROS fold change (Fig. 3 H) as well as the fold change for pool of all endpoints (Fig. 3 K) seemed to correlate inversely with injected activity, yet interpretations should be cautious given the scatter of data. There was a weak trend of positive correlation of effective dose with gene expression, ROS, and the pool of endpoints fold changes (Fig. 4 , Supplemental Table 3). Correlation patterns with activity left in blood at 2 hours post-procedure (Fig. 5 ) or percent of injected activity left at 2 hours (Supplemental Fig. 5) were weak, and in some cases, driven by few individuals such as the apparent negative correlation observed for ROS with activity left in blood at 2 hours (Fig. 5 H, Supplemental Table 4). Supplemental Fig. 6 show correlation analyses of fold changes in γH2AX vs. either gene expression or ROS, as well as correlations of fold changes in ROS vs. either gene expression or ROS plus additional UVA exposure. Numerical values for these correlations are provided in Supplemental Table 6. Results indicated that γH2AX correlated positively with gene expression (Supplemental Fig. 6A-F) and with a weak pattern of negative correlation with ROS fold changes (Supplemental Fig. 6M). Furthermore, there were no clear correlations between ROS and gene expression fold changes, with, maybe, the exception of CDKN1A (Supplemental Fig. 6H), yet with a weak positive correlation. Also, there was no clear correlation between the ROS levels after diagnostic procedure and additional UVA exposure-induced ROS in 2 h samples (Supplemental Fig. 6N). Further analyses were performed to assess the correlation of the observed fold changes with age (Supplemental Fig. 7), body mass (Supplemental Fig. 8), sex (Supplemental Fig. 9), and previous radiotherapy (Supplemental Fig. 10) or chemotherapy treatment (Supplemental Fig. 11). The numerical results are provided in Supplemental Tables 7–11 and demonstrate weak correlations with the observed fold changes. However, the advanced age of a large proportion of patients, the limited number of females included in the study, and few patients with record of previous radiotherapy or chemotherapy regimen make the interpretation of these results difficult. The potential of gene expression, γH2AX foci and ROS as biomarkers of low dose exposure in the absence of a control Because in the event of a radiological emergency control samples are rarely available, and consequently, it is not possible to normalise data to an unexposed biological material, the response of each endpoint was further assessed based on raw data without normalisation to individual samples before the diagnostic procedure (Fig. 6 ). These were: 2 −ΔCt values for gene expression, median γH2AX and median ROS values. To compare results at 2 h vs. 0 h, one-way ANOVA with Šidák correction for multiple comparisons was used for gene expression and ROS since, for these endpoints, more than two factors were analysed (Supplemental Table 12). For γH2AX, paired t-test was used. The 2 −ΔCt values of blood samples at 2 h did not differ statistically from those at 0 h for either PET or scintigraphy patients, Fig. 6 A and 6 B, respectively, and Supplemental Table 12. To increase the statistical power by considering all patients, which could be used to identify cohorts of people exposed to low dose radiation in a radiological emergency, PET-CT and scintigraphy patients were pooled. Gene expression at 2 h samples was compared to that at 0 h based on raw 2 −ΔCt values for all patients pooled (Fig. 6 C). ANOVA results, provided in Supplemental Table 12, indicated that the expression of none of the genes was significantly different from control samples at 2 h. Moreover, 2 −ΔCt values of the pool of genes was not statistically different between these two timepoints based on paired t-test (Fig. 6 D). The median γH2AX intensity differed significantly at 0- and 2 h in scintigraphy patients alone (p = 0.02, 0.42), Fig. 6 F, as well as the pool of all scintigraphy and PET patients (p = 0.004, 0.36), Fig. 6 G, but not in PET patients alone (Fig. 6 E). ROS levels were only significantly different from control values in samples exposed to UVA (Fig. 6 H and I). Both group of patients, separately or pooled (Fig. 6 J), showed different ROS levels in UVA exposed samples at 0 h as compared to 0 h controls, 2 h samples as compared to 0 h controls, and 2 h samples as compared to 2 h controls, Supplemental Table 12. Importantly, when endpoints were pooled either for PET patients only (Fig. 6 K), scintigraphy patients only (Fig. 6 L) or the pool of all patients (Fig. 6 M), exposed samples could not be discriminated from unexposed samples, Supplemental Table 12. Correlation analyses were then performed with raw data at 0- or 2 h as justified below. Raw data at 0 h was correlated with patients’ age (Supplemental Fig. 12), body mass (Supplemental Fig. 13) and sex (Supplemental Fig. 14), as these should, ideally, not determine the response of the analysed endpoints. Consequently, absence of correlation would be desirable as it would indicate high inclusiveness value of the corresponding biomarker. Also, raw data at 0 h would most likely not correlate with previous record of RT (Supplemental Fig. 15) or CHT (Supplemental Fig. 16) provided that these did not occur shortly before. No clear correlations were observed (Supplemental Fig. 12–16) with the exception of a weak inverse correlation of ROS levels after UVA exposure with age (Supplemental Fig. 12I). We further tested how the different endpoints correlated based on raw data at 0 h as these could indicate the activity level of a potential background DNA damage response (Supplemental Fig. 17). With the exception of a weak positive correlation of CDKN1A with γH2AX (Supplemental Fig. 17B), no clear correlations were identified in these analyses. A strong correlation of raw data at 2 h with injected activity (Supplemental Fig. 18), effective dose (Supplemental Fig. 19), blood activity remaining at 2 h (Supplemental Fig. 20) and the percent of injected activity remaining in blood at 2 h (Supplemental Fig. 21) would indicate the potential of these biomarkers to discriminate individuals exposed to low doses, even in the absence of an unexposed control. These results indicated, however, only a weak pattern of increased 2 −ΔCt values, i.e. higher expression, with higher effective doses for CDKN1A (Supplemental Fig. 19B) and XPC (Supplemental Fig. 19F), but with large data scatter. Driven by a few individuals, a weak positive correlation was found for ROS levels with effective dose (Supplemental Fig. 19H) and for BBC3 expression with activity in blood at 2 h (Supplemental Fig. 20A). Finally, correlation analyses between endpoints for raw data at 2 h (Supplemental Fig. 22), which would ideally represent the coordinated induction of the DNA damage response at different levels after exposure to these low doses, resulted in generally flat slopes, and low R 2 and r values, with the exception of a positive slope observed for CDKN1A vs. γH2AX (Supplemental Fig. 22B). Discussion DNA damage, ROS levels and gene expression changes induced by PET-CT or scintigraphy exposure were determined using blood collected before and after the diagnostic procedures and correlated to patients’ data to further characterize these endpoints as biomarkers of IR exposure. Although correlation analyses revealed generally mild slopes and low r values, γH2AX fold change presented a weak positive correlation with injected activity, indicating that exposure from these diagnostic procedures induced a subtle increase in DNA damage (Fig. 3 ). To note, each individual was sampled before and after procedure, acting as his/her own control, being a strength of this study. Consistent with the overall weak fold changes at the level of γH2AX, as well as ROS, the expression changes in the panel of radiation-responsive genes tested at 2 h post-procedure were also generally low relative to control samples (Fig. 2 ). Some patients did show consistent upregulation of several endpoints, e.g. patient P-12, S-06 and P-05, while others showed downregulation, e.g. patients P-06 and P-02. The reason for variability in the response between patients who received comparable injected activities or effective doses remains unclear. P-12 and S-06, who were among those with highest γH2AX levels (DNA damage data were not available for P-05) showed, together with P-05, the highest gene expression upregulation. The effective doses received by P-12, S-06 and P-05 were in the high range. S-06, whose effective dose was lower than P-12 or P-05, had, nevertheless, received a high injected activity and showed a high remaining activity at 2 h (Fig. 2 A and Table 1 ). Conversely, P-06 and P-02 showed gene expression downregulation and reduced ROS levels (if available) relative to control, while their effective doses were also high, yet had intermediate injected activity in relative terms (Fig. 2 A). For internal exposures, the biokinetics and radionuclide decay have a strong impact on gene expression in white blood cells, which indeed correlates best with the kinetics of dose decay (exponential decay of activity) rather than with total absorbed dose (Edmondson et al. 2016 ). This, however, did not seem to explain the observed differences between P-12, S-06 and P-05 with P-06 and P-02 patients considering the comparable amount of activity lost during the 2 h gap since administration. The different responses observed in patient samples could also be related to diverse pathophysiological stages (Whitney et al. 2003 ), different individual radiosensitivity (Badie et al. 2008 ) and/or variable activation of the DDR following low doses (Lee et al. 2015 ). A panel of genes predictive for radiation toxicity has been previously described (Rieger et al. 2004 ), although it does not include the genes analysed here. P-05 had previously undergone radiotherapy, but information regarding tissue reaction was not available. Up- or downregulation patterns have been observed earlier for different individuals undergoing interventional imaging procedures for the CDKN1A , FDXR , GADD45A and MDM2 genes (Visweswaran et al. 2019 ), and in patients undergoing SPECT myocardial perfusion imaging (MPI) for the MDM2 and BBC3 genes (Lee et al. 2015 ). Furthermore, the relative induction of CDKN1A and GADD45A ranged between ca 2- to- 7-fold and between 1- to 7- fold, respectively, in patients diagnosed with different malignancies 6 h after the first 1.5 Gy fraction of TBI (Amundson et al. 2004 ). Some degree of variation in FDXR expression was observed in healthy human donors even at 0 h (O'Brien et al. 2018 ), and in lymphoblastoid cells from different individuals 12 h post 10 Gy exposure, for CDKN1 and GADD45A in addition to FDXR (Jen and Cheung 2003 ). It seems that there is a lower interindividual variation for FDXR , which is expressed at a low level endogenously (Manning et al. 2013 ), than for CDKN1A (Abend et al. 2016 ). However, large interindividual variability in some FDXR variants appear in response to TBI exposure, with CVs between 19.6–46 depending on the variant (Cruz-Garcia et al. 2020 ). Although not within the scope of this study, it would be interesting to monitor these patients for tissue response provided upcoming radiotherapy treatment. Also, to conduct genetic analyses, as polymorphisms in the trans regulators of radiation-induced expression genes are major phenotype-determinants, only < 1% controlled by cis -acting regulators (Smirnov et al. 2009 ). To statistically compare the changes observed at 2 h to those at 0 h, PET and scintigraphy patients were pooled, first per group, and then altogether. This approach was justified not only from the perspective of gaining statistical power, but importantly, because radiological emergencies may involve a wide range of doses, radiation qualities, and individuals with unique profiles. IR exposure biomarkers should, ideally, discriminate exposed and unexposed individuals in a heterogenous population independently, or with a moderate- to- low impact, of disease or infection status (O'Brien et al. 2018 ; Paul et al. 2011 ), prior exposure to chemotherapy (Lucas et al. 2014 ), sex (Cruz-Garcia et al. 2018 ; Cruz-Garcia et al. 2020 ; Lucas et al. 2014 ; O'Brien et al. 2018 ) and anti-oxidant levels (O'Brien et al. 2018 ). Age and sex contribute, for example, less than 20–30% to the total inter-individual variance in gene expression, which is considered negligible, as their impact on fold changes still fall within the two-fold equivalent to control values (Agbenyegah et al. 2018 ). A non-significant trend of increased H2AX, p53 and ATM phosphorylation was observed in lymphocytes from 20–25-year-old individuals compared to a 40–55 age group following a 25 mGy dose (Lee et al. 2015 ). In our study, correlations of γH2AX, gene expression, and ROS fold changes with age (Supplemental Fig. 7) were unclear, partly due to the bias towards older individuals. Moreover, no clear correlations were observed between raw data at 0 h and body mass (Supplemental Fig. 13), sex (Supplemental Fig. 14), previous record of RT (Supplemental Fig. 15) or CHT (Supplemental Fig. 16) indicating a high inclusiveness value of the studied biomarkers, albeit with the limitation of small sample size for some variables. Besides, with the exception of a weak positive correlation of CDKN1A with γH2AX at 0 h (Supplemental Fig. 17B), no clear correlations between endpoints were identified at 0 h which would suggest an active DNA damage response in these patients before the diagnostic examinations (Supplemental Fig. 17). Coherent with the higher activity administered to the scintigraphy patients (mean injected activity of 731 ± 17.5 MBq) than to the PET-CT patients (277 ± 61.7 MBq), and the twice as high mean activity left in blood at 2 h in the scintigraphy patients, scintigraphy induced a statistically significant upregulation of γH2AX levels as compared to control (Fig. 2 D). This was not detected for PET-CT, despite a medium effect size. The statistical significance of γH2AX fold change at 2 h was lost when scintigraphy and PET groups were pooled (Fig. 2 F). However, the induction of γH2AX still correlated positively with gene expression fold changes (Supplemental Fig. 6A-F). In line with this finding, up-regulation of γH2AX (Halm et al. 2014 ; Rothkamm et al. 2007 ; Vandevoorde et al. 2015 ) and FDXR expression (O'Brien et al. 2018 ) were detected after low computed tomography doses. Also, those SPECT MPI patients with increased γH2AX after procedure showed significant upregulation of DDR-related genes such as Tp53 and MDM2 (Lee et al. 2015 ). Using blood samples exposed ex vivo with a CT scanner, both radiation-induced foci (RIF) and FDXR expression increased linearly with dose at comparable unit rates, with a significant increment relative to control at 11.3 mGy up to 49.7 mGy for a 3-fold RIF, and at 22.6 mGy up to 49.7 mGy for a 4-fold FDXR expression (Schule et al. 2023 ). Consistent with a generally low level of induced DNA damage, only weak trends of gene expression upregulation relative to control were observed. These included BBC3 , XPC and GADD45A genes in both groups, and CDKN1A , FDXR and MDM2 in PET-CT patients only (Fig. 2 B). BBC3 showed medium effect size (with large p-value) in scintigraphy and PET patients, which also applied to FDXR and XPC in PET patients only. FDXR is one of the most IR-responsive genes in PBMC (Cheng et al. 2019 ; Manning et al. 2013 ) and among those with the best dose discrimination power (Lacombe et al. 2018 ). BBC3, XPC and CDKN1A are also identified as top predictor genes of radiation response in humans (Dressman et al. 2007 ). The upregulation of these genes could indicate: an initial cell cycle arrest through CDKN1A (Brugarolas et al. 1995 ) and GADD45A (Wang et al. 1999 ) (albeit PBMC are not dividing); a pro-apoptotic response in heavily damaged cells, led by BBC3 (Chipuk et al. 2005 ; Jeffers et al. 2003 ) and FDXR (Hwang et al. 2001 ; Liu and Chen 2002 ; Zhang et al. 2017 ) and the activation of DNA damage repair through XPC (Adimoolam and Ford 2002 ; Sugasawa et al. 1998 ). For both groups together, BBC3 and XPC expression at 2 h remained with a medium effect size (Fig. 2 F). The weak gene expression changes observed on average here are in agreement with previously reported small changes in FDXR expression (1.3-1.7-fold change) in 6 out of 8 patients at 2 h after CT exposure with estimated doses to the blood of 3.9–20.9 mGy (O'Brien et al. 2018 ). CDKN1A showed a ca 27-fold increase while GADD45A remained close to control levels 6 h after 1.5 Gy delivered by TBI treatment in a non-Hodgkin’s lymphoma patient (Amundson et al. 2004 ). CDKN1A and GADD45A showed a ≤ 2-fold upregulation relative to control 2 h after CT scan with estimated doses of 10–43 mGy and after 6 mGy 18 F-FDG injection followed by a 0.2 cGy (2 mGy) CT scan (Riecke et al. 2012 ). FDXR was, however, significantly upregulated 24 h after TBI, and continuously during fractionated treatment for several malignancies (O'Brien et al. 2018 ). Following the first fraction with a 0.038–0.169 Gy dose to the blood, the mean fold changes in six radiotherapy patients are: 1.45, for FDXR ; 1.67, for CDKN1A ; 1.08, for BBC3 ; 1.09, for MDM2 ; and 1.02, for GADD45 (Cruz-Garcia et al. 2022 ). When considering the overall gene expression response by the pool of genes, PET patients showed a statistically significant upregulation, but not scintigraphy patients, Fig. 2 C. This observation was interesting in light of the already discussed higher activity injected in the scintigraphy group and might reflect dose-dependent kinetics of transcription. Considering the dynamic nature of γH2AX foci (Lee et al. 2015 ) and of gene expression changes (Cruz-Garcia et al. 2022 ; Edmondson et al. 2016 ; Evans et al. 2022 ; Kabacik et al. 2015 ; Macaeva et al. 2019 ), it would have been interesting to study additional time points after exposure. This was, unfortunately, not possible due to sampling constraints for practical reasons in the clinic. It is plausible to assume that a different magnitude of response could have been detected had other timepoints (and/or gene-sets) been chosen. Nevertheless, the available timepoint was justified based on in vivo gene expression changes detected already at 2 h after low doses (Lee et al. 2015 ; O'Brien et al. 2018 ), and, as already discussed, the fold changes are in the range observed by others early after low doses to the blood and correlate with the low level of damage induced. γH2AX foci have been shown to appear 5 minutes after low dose exposure, peak at 15–30 minutes and decrease to baseline levels by 120 minutes (Lee et al. 2015 ). FDXR has been shown to peak at 8 h, with a mean fold change of 2.7 across radiotherapy patients, and becomes significantly upregulated relative to control at 24 h with a mean fold change of 1.8 after the first fraction (Cruz-Garcia et al. 2022 ). Similarly, CDKN1A was shown to increase significantly at 24 h post first radiotherapy fraction, with a mean fold change of 1.6, while neither BBC3 , MDM2 or GADD45A were significantly upregulated over a 24 h time period post low doses to the blood (Cruz-Garcia et al. 2022 ). CDKN1A , FDXR , GADD45A , XPC , and MDM2 were also significantly upregulated (maximum fold change of ca 8.5 for FDXR ) at 72 h post- 131 I- metaiodobenzylguanidine ( 131 I- mIBG) exposure (at doses in the range 2–3 Gy and calculated doses to the blood of 0.45–1.97 Gy) in neuroblastoma paediatric patients as shown in two related studies (Edmondson et al. 2016 ; Evans et al. 2022 ). These genes were also upregulated (6-fold upregulation for FDXR ) in 131 I- mIBG-irradiated patients at 96 h post-exposure, although with a general pattern of downregulation as compared to the 72 h timepoint (Edmondson et al. 2016 ). At 15 days after exposure, the fold changes of CDKN1A and GADD45A were not different from control, while MDM2 and XPC became downregulated (ca 0.7-fold), and FDXR remained upregulated (ca 1.5-fold), indicating that the DDR may be active long after exposure (Evans et al. 2022 ). In agreement with gene expression results, only the PET group showed a medium effect size for ROS upregulation, which was not statistically significant (Fig. 2 E). Moreover, additional exposure of samples to UVA radiation did not induce a significant increase of ROS in scintigraphy patients (p > 0.99, d = 0.01) nor in PET-CT patients, who showed, nevertheless a large effect size with a downregulation pattern (p = 0.82, d = 0.95). This suggests that the diagnostic exposure of patients did not change the impact of UVA irradiation in PBMC. It should be noted, however, that low doses of radiation were reported to induce oxidative stress leading to oxidised nucleotides in the cellular cytoplasm (Haghdoost et al. 2006 ; Sangsuwan and Haghdoost 2008 ). A weak trend of positive correlation of ROS fold change with effective dose was found (Fig. 4 H), but correlations of this endpoint with injected activity (Fig. 3 H), activity remaining in blood at 2 h (Fig. 5 H) or percent of injected activity remaining at 2 h (Supplemental Fig. 5H) were weak or unclear due to large data scatter. That ROS fold changes, gene expression and the pool of endpoints presented a weak but positive correlation with effective dose (Fig. 4 ), manifested the relevance of considering isotope-specific conversion factors to account for different biodistributions, despite the fact that beta-emitters deposit most of their emitted energy locally, i.e. within the blood, tumour or target organs (Edmondson et al. 2016 ). A secondary, yet relevant aspect of our study was the assessment of gene expression, γH2AX foci and ROS as biomarkers of in vivo low dose exposure even in the absence of a control sample, such as following a radiological emergency. In such situations, it has been suggested that cycle threshold (ΔCT) values may serve as exposure indicators provided that RNA amount and quality input are precisely controlled (Edmondson et al. 2016 ). This strategy has been successfully applied ex vivo (Brzoska and Kruszewski 2015 ; Paul and Amundson 2008 ) and in vivo (Abend et al. 2016 ). Non-irradiated and irradiated samples in the range of 1.25 Gy (one fraction) to 3.75 Gy (delivered in three fractions) were discriminated with high accuracy in patients receiving total body irradiation (Dressman et al. 2007 ; Filiano et al. 2011 ; Lucas et al. 2014 ; Meadows et al. 2008 ; Paul et al. 2011 ). Moreover, blood samples of prostate cancer patients receiving intensity-modulated radiation therapy (IMRT) were discriminated from preexposure control samples based on FDXR expression at 24 h after equivalent blood doses as low as 0.09 − 0.017 Gy (Abend et al. 2016 ). Exposed and unexposed samples were also discriminated after internal exposures using a panel of genes including FDXR and CDKN1A (Edmondson et al. 2016 ; Evans et al. 2022 ). The aim here was to test whether 0- and 2 h samples could be discriminated based on raw data (Fig. 6 ). Exposed samples were not significantly different from unexposed samples when using the pool of all endpoints for PET (Fig. 6 K) and scintigraphy patients alone (Fig. 6 L) or all patients pooled (Fig. 6 M). ROS levels only deviated from control values in UVA-exposed samples (Fig. 6 H and I) and none of the individual genes (Fig. 6 C), nor the pool of genes (Fig. 6 D), had a significantly different expression from control samples at 2 h when considering all patients pooled. However, the median γH2AX intensity at 2 h differed significantly from that at 0 h in scintigraphy patients alone (Fig. 6 F) and the pool of all patients (Fig. 6 G). γH2AX foci analysed by immunofluorescence microscopy revealed a significantly enhanced frequency in PBMC after low doses of X-radiation delivered during neuro-interventional procedures (Visweswaran et al. 2020 ), not significant for the increase in γH2AX mean fluorescence intensity in post-diagnostic (observed in 64.5% of patients) and post-therapeutic (50% of patients) neuro-interventional procedures as compared to pre-exposure controls (Visweswaran et al. 2019 ). Also, not significant when analysing the percentage of γH2AX positive cells at 30 min post-SPECT as compared to baseline levels (Lee et al. 2015 ). The γH2AX relative fluorescence intensity was found to correlate poorly with the entrance surface dose values, i.e. absorbed dose by the skin in a particular region or organ, measured with a thermoluminescence dosimeter in patients undergoing neuro-interventional diagnostic (P = 0.199, R 2 = 0.0563) and therapeutic (P = 0.617, R 2 = 0.015) procedures from 9- to-225 mGy (Visweswaran et al. 2019 ). Besides, the expression of CDKN1A (0.55-fold change), MDM2 (0.57-fold) and FDXR (0.84-fold), and GADD45A (1.1-fold) did not differ statistically from control samples 24 h after low doses of neuro-interventional radiological procedures (Visweswaran et al. 2019 ). In line with this, correlation analyses between the tested endpoints for raw data at 2 h (Supplemental Fig. 22) did not reveal a clear induction of the DNA damage response after exposure to the tested low doses when considering raw data without normalisation. The shape of the dose response curve for cellular effects after low doses and low dose rates is largely uncertain. While the radiation protection system is quantitatively valuable, implicit assumptions in risk estimation associated to low doses and protracted IR exposures would benefit from stronger evidence through further experimental data (Kreuzer et al. 2018 ; Shore et al. 2017 ). Biomarkers of IR exposure help to understand the molecular and cytogenetic effects of low doses, to be considered in epidemiological studies (Hall et al. 2017 ; Pernot et al. 2012 ) or biodosimetry applications (Swartz et al. 2010 ). This demands, however, appropriate validation of biomarkers by using biological samples exposed in vivo , whose availability is usually limited for obvious reasons. We further characterized γH2AX, ROS levels and transcriptomic changes as biomarkers of IR exposure in vivo using blood from patients undergoing PET-CT and skeletal scintigraphy. γH2AX fold changes correlated weakly, but positively, with injected activity, indicating a radiation-induced increase in DNA damage with dose. γH2AX upregulation also correlated positively with mild changes in transcription of known radiation-responsive genes, suggesting a coherent activation of the DDR pathway following diagnostic imaging-induced genotoxic stress. For reasons to be determined, some patients showed consistent stronger up- or down-regulation of several endpoints after comparable injected activities or effective doses, which could relate to differences in radiosensitivity and/or DDR activation after low doses (Lee et al. 2015 ). This cohort included 29 males and 5 females, with ages ranging from 41 to 80 and an average age of 66. Given the relatively small population, with a bias towards older and male individuals, it would be highly desirable to expand this study by increasing the number of patients, and, if possible, conducting genetic and radiosensitivity analyses as well as tissue response monitoring, if applicable. Declarations The authors have no relevant financial or non-financial interests to disclose. Acknowledgments The study was partly supported by the Swedish Radiation Safety Authority SSM, grant number SSM2017-112. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Regional Medical Chamber, Kielce, Poland (8 th December 2015/No. 16-D/2015). 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Mutat Res Genet Toxicol Environ Mutagen 844:54–61. doi: 10.1016/j.mrgentox.2019.05.011 Walker RC, Smith GT, Liu E, Moore B, Clanton J, Stabin M (2013) Measured human dosimetry of 68Ga-DOTATATE. J Nucl Med 54 (6):855–860. doi: 10.2967/jnumed.112.114165 Wang XW, Zhan Q, Coursen JD, Khan MA, Kontny HU, Yu L, Hollander MC, O'Connor PM, Fornace AJ, Jr., Harris CC (1999) GADD45 induction of a G2/M cell cycle checkpoint. Proc Natl Acad Sci U S A 96 (7):3706–3711. doi: 10.1073/pnas.96.7.3706 Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, Brown PO (2003) Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U S A 100 (4):1896–1901. doi: 10.1073/pnas.252784499 Zahnreich S, Ebersberger A, Kaina B, Schmidberger H (2015) Biodosimetry Based on gamma-H2AX Quantification and Cytogenetics after Partial- and Total-Body Irradiation during Fractionated Radiotherapy. Radiat Res 183 (4):432–446. doi: 10.1667/RR13911.1 Zhang Y, Qian Y, Zhang J, Yan W, Jung YS, Chen M, Huang E, Lloyd K, Duan Y, Wang J, Liu G, Chen X (2017) Ferredoxin reductase is critical for p53-dependent tumor suppression via iron regulatory protein 2. Genes Dev 31 (12):1243–1256. doi: 10.1101/gad.299388.117 Zwicker F, Swartman B, Sterzing F, Major G, Weber KJ, Huber PE, Thieke C, Debus J, Herfarth K (2011) Biological in-vivo measurement of dose distribution in patients' lymphocytes by gamma-H2AX immunofluorescence staining: 3D conformal- vs. step-and-shoot IMRT of the prostate gland. Radiat Oncol 6:62. doi: 10.1186/1748-717X-6-62 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 19 Jun, 2023 Read the published version in Radiation and Environmental Biophysics → Version 1 posted Editorial decision: Major revision 15 May, 2023 Reviews received at journal 12 May, 2023 Reviewers agreed at journal 04 May, 2023 Reviewers invited by journal 04 May, 2023 Editor assigned by journal 04 May, 2023 Submission checks completed at journal 02 May, 2023 First submitted to journal 28 Apr, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2873007","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":197032693,"identity":"b2cf2e67-c3a2-44b8-b70f-9774eecce76d","order_by":0,"name":"Milagrosa López-Riego","email":"data:image/png;base64,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","orcid":"","institution":"Stockholm University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Milagrosa","middleName":"","lastName":"López-Riego","suffix":""},{"id":197032694,"identity":"f328db12-abcd-42d4-8b00-644a94561d2b","order_by":1,"name":"Magdalena Płódowska","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Płódowska","suffix":""},{"id":197032695,"identity":"a7b07d6e-01be-48c5-9951-0c11984cd3f5","order_by":2,"name":"Milena Lis-Zajęcka","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Milena","middleName":"","lastName":"Lis-Zajęcka","suffix":""},{"id":197032696,"identity":"d840288a-9030-4786-973a-5f93d91c2936","order_by":3,"name":"Kamila Jeziorska","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kamila","middleName":"","lastName":"Jeziorska","suffix":""},{"id":197032697,"identity":"4feaf30d-67c6-4a07-8f6d-e03bae892abd","order_by":4,"name":"Sylwia Tetela","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sylwia","middleName":"","lastName":"Tetela","suffix":""},{"id":197032698,"identity":"87b1c1e2-86d7-4ca2-9173-52840a300161","order_by":5,"name":"Aneta Węgierek-Ciuk","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aneta","middleName":"","lastName":"Węgierek-Ciuk","suffix":""},{"id":197032699,"identity":"5019c33e-a580-4094-8f93-581488cb2708","order_by":6,"name":"Daniel Sobota","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Sobota","suffix":""},{"id":197032700,"identity":"9c117ae4-d543-4259-88d7-4339c89c2a48","order_by":7,"name":"Janusz Braziewicz","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Janusz","middleName":"","lastName":"Braziewicz","suffix":""},{"id":197032701,"identity":"7bb59658-7632-4367-990f-f998cf5c7892","order_by":8,"name":"Lovisa Lundholm","email":"","orcid":"","institution":"Stockholm University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lovisa","middleName":"","lastName":"Lundholm","suffix":""},{"id":197032702,"identity":"4f9caaf6-daa8-4896-9ac1-211ec168c0a3","order_by":9,"name":"Halina Lisowska","email":"","orcid":"","institution":"Jan Kochanowski University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Halina","middleName":"","lastName":"Lisowska","suffix":""},{"id":197032703,"identity":"411d1a94-9498-497f-90c9-4cd7b4e1ae3b","order_by":10,"name":"Andrzej Wojcik","email":"","orcid":"","institution":"Stockholm University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrzej","middleName":"","lastName":"Wojcik","suffix":""}],"badges":[],"createdAt":"2023-04-28 12:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2873007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2873007/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00411-023-01033-4","type":"published","date":"2023-06-19T21:19:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":36651211,"identity":"7319e6f3-0a86-4272-9d3e-110af6b930bd","added_by":"auto","created_at":"2023-05-05 15:13:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1461478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental setup.\u003c/strong\u003eBlood was drawn from 17 PET-CT patients and 17 scintigraphy patients before (0 h) and after (2 h) the corresponding diagnostic procedure with the aim of validating biomarkers of ionising radiation exposure. Gene expression analyses were performed by qRT-PCR on stabilized RNA from whole blood to determine the level of expression of six radiation-responsive genes: \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eMDM2\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e, \u003cem\u003eBBC3\u003c/em\u003e and \u003cem\u003eXPC\u003c/em\u003e. The level of γH2AX and ROS were assessed by flow cytometry, using the 2', 7' – Dichlorofluorescin diacetate (DCFDA) test for the latter. For ROS, blood samples were additionally tested after exposure to UVA at the two time points. Activity of isotopes in 2 h blood samples was measured by a germanium detector. Created with BioRender.com\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/812fd154a8a162c9fd6316aa.png"},{"id":36651789,"identity":"80fc9593-1683-4c97-9668-c39422c944c3","added_by":"auto","created_at":"2023-05-05 15:21:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1613461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFold changes for PET-CT (PET, P) and scintigraphy (S) patients.\u003c/strong\u003e A: heatmap of fold changes for all endpoints in each patient, with individuals ranked by highest to lowest overall responsiveness in gene expression. Donor-matched effective dose (E, mSv), activity (act.) left in blood at 2 h (mBq), and injected activity (inj. act., MBq) relative (R) to a scale 0-1 are shown in the right heatmap panels. Black crosses indicate unavailable data. ROS fold changes \u0026gt;3.5 are shown in brown. B-G represent scatter dot plots for: gene expression (GE) fold changes of each gene (B) or the average of the pool of genes (C); γH2AX\u003cem\u003e \u003c/em\u003efold change (D); ROS\u003cem\u003e \u003c/em\u003efold change after diagnostic procedure only, or after additional UVA exposure (E); fold change of each endpoint for the pool of all patients (F); average fold change of all endpoints pooled per donor (excluding UVA results) (G). ROS (UVA): 2 h samples exposed to UVA relative to 0 h samples exposed to UVA. Each symbol represents one patient. A red horizontal line represents a fold change equivalent to control values (Y=1). p values (top) shown in red if p\u0026lt;0.05 and effect size d values (bottom) shown in green (medium, \u0026gt;0.5-0.8), blue (large, \u0026gt;0.8-1.3) or red (very large, ≥1.3), Supplemental Table 1. Mean and standard deviation shown by red and black bars, respectively (B), or by blue bars (C-G)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/42aafa057f8703202a70d1e6.png"},{"id":36651210,"identity":"06cd41e5-2e89-4882-94aa-ce74b692d219","added_by":"auto","created_at":"2023-05-05 15:13:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1455916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of injected activity (MBq) with fold change results from each endpoint considering the pool of all patients. \u003c/strong\u003eA-F: gene expression fold changes for \u003cem\u003eBBC3\u003c/em\u003e (A), \u003cem\u003eCDKN1A\u003c/em\u003e (B), \u003cem\u003eFDXR\u003c/em\u003e(C), \u003cem\u003eGADD45A\u003c/em\u003e (D), \u003cem\u003eMDM2\u003c/em\u003e (E), and \u003cem\u003eXPC\u003c/em\u003e (F). G: γH2AX fold change. H-I: ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure as compared to control samples at 0 h (H) or ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure and additional UVA exposure as compared to control samples at 0 h exposed to UVA (I). J: average fold change per patient for the pool of genes. K: average fold change per patient for the pool of endpoints (excluding ROS UVA). Each symbol represents one individual. Linear regressions (Supplemental Table 2) are represented with a black solid bar and 95% confidence interval are represented with dotted black bands\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/747076b1ead4ea57d3140fd3.png"},{"id":36651215,"identity":"bc5fa9f4-903e-4079-af47-a2e9b18fb8d6","added_by":"auto","created_at":"2023-05-05 15:13:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1308886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of effective (E) dose (mSv) with fold change results from each endpoint considering the pool of all patients. \u003c/strong\u003eA-F: gene expression fold changes for \u003cem\u003eBBC3\u003c/em\u003e (A), \u003cem\u003eCDKN1A\u003c/em\u003e (B), \u003cem\u003eFDXR\u003c/em\u003e(C), \u003cem\u003eGADD45A\u003c/em\u003e (D), \u003cem\u003eMDM2\u003c/em\u003e (E), and \u003cem\u003eXPC\u003c/em\u003e (F). G: γH2AX fold change. H-I: ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure as compared to control samples at 0 h (H) or ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure and additional UVA exposure as compared to control samples at 0 h exposed to UVA (I). J: average fold change per patient for the pool of genes. K: average fold change per patient for the pool of endpoints (excluding ROS UVA). Each symbol represents one individual. Linear regressions (Supplemental Table 3) are represented with a black solid bar and 95% confidence interval are represented with dotted black bands\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/d50b2c85682281636c6a0ba9.png"},{"id":36651214,"identity":"31f7a762-48ad-4dda-9ba5-e3a836e7f684","added_by":"auto","created_at":"2023-05-05 15:13:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1501982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of blood activity left (mBq) with fold change results from each endpoint considering the pool of all patients. \u003c/strong\u003eA-F: gene expression fold changes for \u003cem\u003eBBC3\u003c/em\u003e (A), \u003cem\u003eCDKN1A\u003c/em\u003e (B), \u003cem\u003eFDXR\u003c/em\u003e(C), \u003cem\u003eGADD45A\u003c/em\u003e (D), \u003cem\u003eMDM2\u003c/em\u003e (E), and \u003cem\u003eXPC\u003c/em\u003e (F). G: γH2AX fold change. H-I: ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure as compared to control samples at 0 h (H) or ROS fold changes in blood samples 2 h after PET (P) or scintigraphy (S) procedure and additional UVA exposure as compared to control samples at 0 h exposed to UVA (I). J: average fold change per patient for the pool of genes. K: average fold change per patient for the pool of endpoints (excluding ROS UVA). Each symbol represents one individual. Linear regressions (Supplemental Table 4) are represented with a black solid bar and 95% confidence interval are represented with dotted black bands\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/d03c4c617b1f7ec6597b2845.png"},{"id":36651212,"identity":"e798a48c-4214-4237-b7e2-f61bd3f0ad4d","added_by":"auto","created_at":"2023-05-05 15:13:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2439567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRaw data (2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-ΔCt\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) at 0 and 2 h for PET (P) and scintigraphy (S) patients.\u003c/strong\u003e A-D: scatter dot plots representing 2\u003csup\u003e-ΔCt\u003c/sup\u003e gene expression for individual genes in PET patients (A), scintigraphy patients (B), pool of patients (C) and the pool of genes for all patients (D). Mean and standard deviations represented by red and black bars, respectively (A-C), or by red and blue bars, respectively (D-I). E-G median γH2AX fluorescence intensity for PET patients (E), scintigraphy (F), or the pool of patients (G). H-J: median ROS levels for PET patients (H), scintigraphy (I), or the pool of patients (J). ROS UVA: ROS after UVA exposure in 0 h samples. ROS P/S+UVA: ROS after UVA exposure in 2 h samples. K-M: scatter dot plot for the average raw results of all endpoints pooled (excluding UVA results) at 0 and 2 h for PET patients (K), scintigraphy patients (L) or the pool of patients (M), whereby the following factors were applied to raw data to get them to the same range (adjusted scale): \u003cem\u003eBBC3\u003c/em\u003e (x500000); \u003cem\u003eCDKN1A\u003c/em\u003e (x1000000); \u003cem\u003eFDXR\u003c/em\u003e (x1000000); \u003cem\u003eGADD45A\u003c/em\u003e (x1000000); \u003cem\u003eMDM2\u003c/em\u003e: (x300000); \u003cem\u003eXPC\u003c/em\u003e (x1000000); γH2AX (÷100); ROS (÷1500). p values (left) are shown if p\u0026lt;0.05 and effect size d values (right) are shown in green (medium, \u0026gt;0.5-0.8), blue (large, \u0026gt;0.8-1.3) or red (very large, ≥1.3), Supplemental Table 12. Each symbol represents one patient\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/b3115b5f784335c8869d8be8.png"},{"id":44733894,"identity":"3e4291af-4486-4097-8c43-a7de620d919f","added_by":"auto","created_at":"2023-10-16 22:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2002545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/44724069-5bf6-4799-ad37-2552744f7194.pdf"},{"id":36651216,"identity":"3db345a1-5f21-4a99-95e0-edb775522886","added_by":"auto","created_at":"2023-05-05 15:13:03","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4453562,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2873007/v1/5d58280eaaa263b1edf36926.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFollowing genotoxic stress induced by the direct action of ionising radiation and, indirectly, by reactive oxygen species (ROS), the DNA damage response (DDR) is activated to preserve the integrity of the genome. Oxidative stress from water radiolysis is further amplified by ROS-producing cellular systems such as mitochondria (Reisz et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Szumiel \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Early on in the DDR cascade, serine 139 of histone H2AX (γH2AX) is phosphorylated, which signals the presence of DNA double strand breaks (DSBs) (Rogakou et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), one of the most deleterious DNA lesions (Schipler and Iliakis \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Downstream, a complex network of pro-survival or pro-death genes, usually p53-controlled (Hu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), interact to determine the cellular fate (Christmann and Kaina \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Roos et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) both after environmentally-relevant (Amundson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sokolov and Neumann \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and high doses (Beer et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; El-Saghire et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Exploiting these molecular changes and the cytogenetic end-products that may follow ionising radiation (IR) exposure, a range of IR biomarkers have been proposed for use in biodosimetry (Swartz et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) or epidemiological studies (Hall et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pernot et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The validation of these IR biomarkers requires appropriate human models.\u003c/p\u003e \u003cp\u003eNumerous validation efforts have been published for the γH2AX assay (Ainsbury et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barnard et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rothkamm et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and gene expression (Abend et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abend et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Badie et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Biolatti et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Manning et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as IR biomarkers sensitive to doses in the mGy range (Schule et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). γH2AX was tested as a biomarker of DNA damage and repair in human population studies related to diagnostic procedures (Brand et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Halm et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kuefner et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lobrich et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pathe et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rothkamm et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vandevoorde et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), chemotherapy (Halicka et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Karp et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sak et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and/or radiotherapy (Sak et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zahnreich et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zwicker et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), as previously reviewed (Valdiglesias et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The application of gene expression profiles in epidemiological studies has so far been limited by their transient nature (Hall et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, transcription studies provide a valuable source of information for our understanding of cellular response to low doses (Sokolov and Neumann \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), a dose range where stochastic effects are poorly defined due to larger uncertainties of epidemiological data (Kreuzer et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Being able to reflect radiation exposure over a wide range of doses (Amundson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Amundson and Fornace \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Manning et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), transcriptomic biomarkers can also support dose reconstruction (Ghandhi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e), triage (Port et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and clinical outcome prediction (Port et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in the event of radiological emergency. Fast readout, within hours to days, the possibility of high-throughput analysis (Ostheim et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ease of sampling and high sensitivity early after exposure (Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) add substantial value to their use in biodosimetry. In addition, the development of a transcriptomic dosimeter could help in estimating internal doses in radionuclide therapy and internal contamination, which currently relies on whole body counting, biokinetic models as well as bioassays on urine or faecal samples (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Due to ethical considerations and given the restricted availability of suitable human samples, transcriptomic IR biomarker characterization usually entail mice (Ghandhi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e), non-human primate models (Park et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Port et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and, to a large degree, human \u003cem\u003eex vivo\u003c/em\u003e exposed samples (Abend et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Badie et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kaatsch et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kaatsch et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kabacik et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Knops et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Manning et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nosel et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Paul and Amundson \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schule et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although these are relevant models to the human \u003cem\u003ein vivo\u003c/em\u003e response (Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and help to understand the potential impact of cofounding factors, e.g. inflammation (Mukherjee et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or DNA repair capacity (Rudqvist et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the problems of interspecies differences (Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Satyamitra et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), blood cell deterioration, the absence of tissue signalling (Ghandhi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) and cellular microenvironment (Filiano et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in culture are acknowledged. Consequently, candidate IR transcriptomic biomarkers must be validated in humans exposed \u003cem\u003ein vivo\u003c/em\u003e (Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe number of gene expression studies using \u003cem\u003ein vivo\u003c/em\u003e-IR exposed human blood samples from either occupational (Fachin et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Morandi et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sakamoto-Hojo et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), environmental (of natural or accidental origin) (Albanese et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jain and Das \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), diagnostic or therapeutic exposures (Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Amundson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Campbell et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dressman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Filiano et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meadows et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Port et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Riecke et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) is limited, but the available results clearly demonstrate the usefulness of transcript signatures as biomarkers of radiation exposure. Differential gene expression profiles are detected in human peripheral blood mononuclear cells (PBMCs) isolated from X- and γ-radiation-exposed health care workers exposed to doses\u0026thinsp;\u0026lt;\u0026thinsp;25 mSv (Morandi et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Also, an overrepresentation of DDR and p53-related genes is found among differentially expressed genes (DEGs) in PBMC of individuals living in high natural background radiation areas (Jain and Das \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, \u003cem\u003eCDKN1A\u003c/em\u003e is 1.5 fold upregulated in individuals exposed to \u0026gt;\u0026thinsp;15 mGy/year and \u003cem\u003eMDM2\u003c/em\u003e is 1.5-fold upregulated in those exposed to 5\u0026ndash;15 mGy/year (Jain and Das \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Differential expression of p53 target genes is also observed in samples from patients exposed, primarily, to external radiation (Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Amundson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dressman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Filiano et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meadows et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Port et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Riecke et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with some exceptions (Campbell et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Fold changes of ca 16, 7, and 3 are observed on average for \u003cem\u003eFDXR, CDKN1A\u003c/em\u003e, and \u003cem\u003eBBC3\u003c/em\u003e, respectively, in patients exposed to 1.25 Gy total body irradiation (TBI) (Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). \u003cem\u003eFDXR\u003c/em\u003e upregulation was shown to increase with dose from diagnostic CT scans to TBI and radiotherapy (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study we sought to correlate the radiation dose with the expression of \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eBBC3\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e, \u003cem\u003eXPC\u003c/em\u003e and \u003cem\u003eMDM2\u003c/em\u003e along with levels of γH2AX and ROS in PBMC of patients undergoing positron emission tomography - computed tomography scan (PET-CT) and skeletal scintigraphy (scintigraphy). Blood was collected before and 2 h after the diagnostic intervention so that the individual background and radiation-induced signal levels could be compared. Additionally, correlation analyses with available individual information such as age, sex, or records of previous radiotherapy or chemotherapy treatment were performed to assess the impact of these factors on the studied biomarkers of exposure.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDonor information and blood sample collection\u003c/h2\u003e \u003cp\u003eBlood samples were obtained at the Department of Nuclear Medicine with Positron Emission Tomography Unit of the Holy Cross Cancer Centre in Kielce (Poland) from patients undergoing diagnostic PET-CT (n\u0026thinsp;=\u0026thinsp;17) and scintigraphy (n\u0026thinsp;=\u0026thinsp;17). Sampling was carried out randomly during the period of March-June 2022 mostly on Monday and Tuesday, in the morning hours. On one day, between two and three patients were sampled that underwent the diagnostic procedure consecutively. Blood was collected by venipuncture in EDTA tubes (BD Vacutainer) and into PAXgene\u0026reg; Blood RNA tubes (BD Biosciences) before (0 h) and after (2 h) the diagnostic procedure. Each blood sample was coded with a letter corresponding to the procedure (P for PET-CT and S for skeleton scintigraphy), a number ranging from 1 to 17 and the blood collection time point (0 and 2). Blood was collected separately for gene expression analysis and the other endpoints. For gene analysis and activity measurements 2.5 ml blood was directly collected into PAXgene\u0026reg; Blood RNA tubes (see below for details), for the other endpoints \u0026ndash; into EDTA tubes. Samples were stored at room temperature and were transported to the Jan Kochanowski University (Kielce) between 30 and 60 min after the 2 h blood sampling. Transport to the university took 15\u0026ndash;20 min. After arrival radioactivity was measured in the PAXgene\u0026reg; tubes at room temperature. The tubes were then gradually frozen according to guidelines of the manufacturer (-20\u0026deg;C followed by -80\u0026deg;C), shipped in two batches on dry ice to Stockholm University (September 2022 and December 2022) and stored there at -80\u0026deg;C until further processing. EDTA samples were processed for the endpoints described below within 60 min of their arrival.\u003c/p\u003e \u003cp\u003eFor analysis of γH2AX foci and ROS, peripheral blood mononuclear cells (PBMC) from ca 5 ml of each blood sample were isolated by gradient centrifugation. To this end blood was diluted 1:1 in phosphate buffered saline (PBS) and overlaid on LymphoprepTM (Serumwerk Bernburg AG for Alere Technologies AS, Oslo, Norway) and centrifuged at 400 x g for 30 min. The layer containing lymphocytes was removed and washed three times with phosphate-buffered saline (PBS). Cells were counted and 1/3 was used for analysis of γH2AX and 2/3 for analysis of ROS as described in detail below.\u003c/p\u003e \u003cp\u003eThe general experimental setup is graphically shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Available information regarding the individuals included in this study such as sex, age, body mass or records of previous radiotherapy (RT) or chemotherapy (CHT) is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cohort included 29 males and 5 females, with ages ranging from 41 to 80, with an average age of 66. For scintigraphy imaging, the \u003csup\u003e99m\u003c/sup\u003eTc- methylene diphosphonate (\u003csup\u003e99m\u003c/sup\u003eTc-MDP) was used. PET patients were diagnosed with squamous cell carcinoma (SCC), melanoma, sarcoma, thymus cancer, head and neck cancer (HNC), lung cancer, bronchus cancer, neoplasm of uncertain behaviour of trachea, bronchus and lung (D38.1), Hodgkin lymphoma, pancreas cancer, prostate cancer or malignant neoplasm of endocrine gland (C75.9). They received either \u003csup\u003e18\u003c/sup\u003eF-Fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG), [\u003csup\u003e18\u003c/sup\u003eF]-labeled prostate-specific membrane antigen (\u003csup\u003e18\u003c/sup\u003eF-PSMA) or \u003csup\u003e68\u003c/sup\u003eGa-DOTA-Phe1 Tyr3-octreotate (\u003csup\u003e68\u003c/sup\u003eGa-DOTATATE). The study was approved by the ethical committee of the Regional Medical Chamber in Kielce.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene expression analysis\u003c/h2\u003e \u003cp\u003eRNA extraction was performed using the PAXgene Blood RNA Kit (PreAnalytiX), following the manufacturer\u0026acute;s instructions. cDNA was synthesised using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) from 95 ng RNA, to maximise the RNA input in the reaction based on the lowest RNA concentration sample. For real time PCR, duplicate reactions of primers, cDNA and 5x HOT FIREPol\u0026reg; EvaGreen\u0026reg; qPCR Supermix (Solis BioDyne) were setup and run on a LightCycler\u0026reg; 480. The cycling conditions were: 95\u0026deg;C (15 min), 40 cycles of 95\u0026deg;C (15 s), 60\u0026deg;C (20 s) and 72\u0026deg;C (20 s). The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method was used for calculation of relative expression, using the housekeeping \u003cem\u003e18S\u003c/em\u003e gene for normalisation. Primer specificity was confirmed using melting curve analysis. Forward (for) and reverse (rev) primers (5\u0026acute;- 3\u0026acute;) used were described earlier for genes of interest (Cheng et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and housekeeping 18S (Lundholm et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These were: \u003cem\u003eGADD45a_\u003c/em\u003efor (actgcgtgctggtgacgaat), \u003cem\u003eGADD45a_\u003c/em\u003erev (gttgacttaaggcaggatccttcca), \u003cem\u003eBBC3\u003c/em\u003e_for (tacgagcggcggagacaaga), \u003cem\u003eBBC3\u003c/em\u003e_rev (gcaggagtcccatgatgagattgtac), \u003cem\u003eMDM2\u003c/em\u003e_for (tatcaggcaggggagagtgataca), \u003cem\u003eMDM2\u003c/em\u003e_rev (ccaacatctgttgcaatgtgatggaa), \u003cem\u003eXPC\u003c/em\u003e_for (gcttggagaagtaccctacaagatggt), \u003cem\u003eXPC\u003c/em\u003e_rev (ggctttccgagcacggttaga), \u003cem\u003eFDXR\u003c/em\u003e_for (tggatgtgccaggcctctac), \u003cem\u003eFDXR\u003c/em\u003e_rev (tgaggaagctgtcagtcatggtt), \u003cem\u003eCDKN1A\u003c/em\u003e_for (cctggagactctcagggtcgaaa), \u003cem\u003eCDKN1A\u003c/em\u003e_rev (gcgtttggagtggtagaaatctgtca), \u003cem\u003e18S\u003c/em\u003e_for (gcttaatttgactcaacacggga), \u003cem\u003e18S\u003c/em\u003e_rev (agctatcaatctgtcaatcctgtcc). Gene expression results here represent an average of the response of all leukocytes, including peripheral blood lymphocytes and granulocytes, lysed in the PAXGene system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eγH2AX analysis\u003c/h2\u003e \u003cp\u003eIsolated PBMC were washed once in PBS and fixed for 10 minutes in Cytofix Fixation Buffer (Becton Dickinson, cat. no. 554655). Cells were washed again in PBS, 90% methanol (Chempur, Poland, chilled at -20\u003csup\u003eo\u003c/sup\u003eC) which was added drop by drop and left for permeabilization for 5 minutes. Cells were washed in Perm/Wash Buffer (Becton Dickinson, cat. no. 554723), incubated with Alexa Fluor 647 Mouse anti-H2AX pS139 (Becton Dickinson, cat. no. 560447) for 60 minutes and washed with Perm/Wash. Cells were resuspended in 300 \u0026micro;l Stain Buffer (FBS, Becton Dickinson, cat. no. 554656) and the level of γ-H2AX fluorescence was measured with a LSR II flow cytometer (Becton Dickinson USA). Alexa Fluor 647 was excited by the red laser (627\u0026ndash;640 nm) and detected using an optical filter centered near 520-nm (e.g., a 660/20 nm bandpass filter). The BD FACS DiVa (version 6.0, Becton Dickinson) was used for data acquisition and analysis. 20,000 events were stored. Per sampling time and patient, the median focus intensity was calculated and used for analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eROS analysis\u003c/h2\u003e \u003cp\u003eOxidative stress induced by UVA was quantified with the help of the 2', 7' \u0026ndash; Dichlorofluorescin diacetate (DCFDA) test (Sigma Aldrich, D6883). PBMC incubated for 15 min in the stain buffer at 37\u003csup\u003e○\u003c/sup\u003eC), then DCF was added for 30 min. Cells were split into two Petri dishes. One was irradiated with UVA on ice (see below) and the other was sham exposed. Next, the cells were transferred to cytometer tubes and the level of fluorescence was measured with a LSR II flow cytometer (Becton Dickinson, USA). A computer system BD FACS DiVa (version 6.0, Becton Dickinson) was used for data acquisition and analysis. Data for 20,000 events were stored. Per sampling time and patient, the median signal intensity was calculated and used for analyses.\u003c/p\u003e \u003cp\u003eUV irradiation was carried out with a 2G11 55 W DULUX L BL lamp, OSRAM, Germany, operating in the UVA range (315\u0026ndash;400 nm). The irradiation time was 20 min and the UV dose was 0.3 kJ/cm\u003csup\u003e2\u003c/sup\u003e. Dosimetry was carried out with a CHY 732 320\u0026ndash;400 nm UVA meter, CHY FIREMATE Co., LTD, UK. The dose was selected based on unpublished results from student projects where it was found to induce a strong signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eActivity measurements and effective dose calculations\u003c/h2\u003e \u003cp\u003eThe activity of blood in each PAXgene\u0026reg; tube was measured using a nitrogen cooled high purity germanium (HPGe) detector (model GX3020 - b12075) placed in a shielded container and connected to a Genie\u0026trade; 2000 Spectroscopy Software, Canberra Industries, Inc, USA. Prior to measuring the activity of blood samples, the spectrometer was pre-calibrated with calibration sources containing \u003csup\u003e99m\u003c/sup\u003eTc and \u003csup\u003e18\u003c/sup\u003eF isotopes of known activity. The activity of the blood samples was measured approximately 2\u0026ndash;4 h after sampling 2 h samples and converted to the sampling time based on the half-life of the specific radioisotope.\u003c/p\u003e \u003cp\u003eThe injected radionuclide activities were documented for each patient and converted to effective doses using the isotope-dependent conversion factors (mSv/mBq): 0.0066 for \u003csup\u003e99m\u003c/sup\u003eTc-MDP (Batista da Silva et al. Congresso Brasiliano de Metrologia das Radiacoes Ionizantes, Rio de Janeiro, 28.11.2018) 0.027 for \u003csup\u003e18\u003c/sup\u003eF-FDG (ICRP \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), 0.022 for \u003csup\u003e18\u003c/sup\u003eF-PSMA (Giesel et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and 0.0257 for \u003csup\u003e68\u003c/sup\u003eGa-DOTATATE (Walker et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eResults from 0 h collection times were compared to 2 h using paired t-tests or one-way ANOVA with multiple comparison corrections. Results from scintigraphy and PET patients were compared using unpaired t-tests. P-values are provided together with Cohen\u0026acute;s effect size d-values, in accordance with the claim that scientific conclusions should not be solely based on significance tests (Amrhein et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The following criteria were applied for effect sizes: d\u0026thinsp;\u0026lt;\u0026thinsp;0.5: small effect; d\u0026thinsp;=\u0026thinsp;0.5\u0026ndash;0.8: medium effect; d\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;1.3: large effect; d\u0026thinsp;\u0026gt;\u0026thinsp;1.3: very large effect (Cohen \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)T-tests (paired and unpaired), one-way ANOVA, linear regression analyses (Y\u0026thinsp;=\u0026thinsp;slope*X\u0026thinsp;+\u0026thinsp;Y-intercept), and correlation analyses to obtain Pearson r- values were performed using GraphPad Prism 9.4.1. Detailed results of the analyses are provided in supplemental tables as specified in the text and figure legends.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCohort information.\u003c/b\u003e 17 PET-CT (P) patients treated with \u003csup\u003e18\u003c/sup\u003eF-FDG, \u003csup\u003e18\u003c/sup\u003eF-PSMA and \u003csup\u003e68\u003c/sup\u003eGa-DOTATATE and 17 skeletal scintigraphy (S) patients treated with \u003csup\u003e99m\u003c/sup\u003eTc-MDP were included in this study. Each individual was coded with a letter attending to the corresponding diagnostic procedure (P/S) and a number 1\u0026ndash;17. Information regarding: sex, age, body mass, diagnosis, year of previous radiotherapy (RT) or chemotherapy (CHT) treatment, if applicable, and injected activity was recorded. SCC: squamous cell carcinoma. H\u0026amp;N: head and neck. D38.1: Neoplasm of uncertain behaviour of trachea, bronchus and lung. C75.9: Malignant neoplasm of endocrine gland, unspecified. Effective dose (E dose, mSv/mBq) calculated using the following conversion factors: 0.0066 (for \u003csup\u003e99m\u003c/sup\u003eTc-MDP), 0.027 (\u003csup\u003e18\u003c/sup\u003eF-FDG), 0.022 (\u003csup\u003e18\u003c/sup\u003eF-PSMA) and 0.0257 (\u003csup\u003e68\u003c/sup\u003eGa-DOTATATE). Activity left in blood at 2 hours post-treatment was measured with a HPGe detector in Bq and converted to mBq, corresponding to numerical values shown in the blood analysis activity column. The percent of injected activity left was calculated based on the injected activity and the activity left in blood at 2 hours\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInjected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBlood analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003erecord\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003erecord\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eactivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003edose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eactivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(M/F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(MBq)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(mSv)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(mBq)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"16\" rowspan=\"17\"\u003e \u003cp\u003e\u003cb\u003eScintigraphy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"16\" rowspan=\"17\"\u003e \u003cp\u003e\u003csup\u003e99m\u003c/sup\u003eTc-MDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScintigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"16\" rowspan=\"17\"\u003e \u003cp\u003e\u003cb\u003ePET\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLung C, SCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThymus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH\u0026amp;N C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLung C, SCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBronchus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHodgkin L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePancreas C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-PSMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProstate Ca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProstate Ca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProstate Ca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProstate Ca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProstate Ca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa - DOTATATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003ePET-CT and scintigraphy induce weak fold changes in gene expression, γH2AX foci and ROS relative to unexposed samples\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGene expression of a panel of six radiation-responsive genes was analysed in PBMC by qPCR before (0 h) and 2 h after PET-CT and scintigraphy. The fold change of each gene at 2 h relative to control (0 h) was calculated for each of the 17 patients per group. PBMC were also analysed for DNA damage by the γH2AX focus test by flow cytometry and for ROS levels by the DCFDA test using flow cytometry. For ROS analysis, aliquots of PBMC collected at 0 h and 2 h were exposed to UVA to determine the possible impact of PET-CT and scintigraphy on the response of cells to oxidative stress induced by a strong oxidative insult. The activity of radionuclides in the blood collected at 2 h was measured with a germanium detector (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to graphically visualise summarised results of all assays, a heat map was constructed where results of all assays are presented as fold changes of data from 2 h over 0 h. Patients were ranked from highest to lowest with respect to fold gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Per patient, the six analysed genes responded fairly similarly as can be judged by the relatively uniform horizontal colour patterns: orange at top rows and blue at bottom rows. No obvious relationship can be seen at this projection between gene expression, γH2AX, ROS and activity measurements. High effective dose values (black and dark grey boxes) clustered in the top 17 rows, suggesting a positive relationship with gene expression. More results presented as fold changes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and described in greater details below.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows the individual and mean results per gene and patient group. None of the genes was significantly upregulated at 2 h, but some showed a medium effect size such as \u003cem\u003eBBC3\u003c/em\u003e in PET-CT patients (p\u0026thinsp;=\u0026thinsp;0.41, d\u0026thinsp;=\u0026thinsp;0.74) and scintigraphy patients (p\u0026thinsp;=\u0026thinsp;0.75, d\u0026thinsp;=\u0026thinsp;0.63), \u003cem\u003eFDXR\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.78, d\u0026thinsp;=\u0026thinsp;0.55) and \u003cem\u003eXPC\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.87, d\u0026thinsp;=\u0026thinsp;0.69) in PET-CT patients only. Unpaired t test was used in order to test whether the PET-CT and scintigraphy patients differed in the gene expression response. For none of the genes there was a statistically significant difference, Supplemental Table\u0026nbsp;1. Effect sizes were small, with the exception of \u003cem\u003eFDXR\u003c/em\u003e, for which a medium effect size (d\u0026thinsp;=\u0026thinsp;0.52) was detected. In an attempt to test if the diagnostic radiation exposure had an impact on overall gene expression per patient, the average fold changes of all 6 genes were calculated. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. The mean fold change of all genes per patient was somewhat higher and more spread out in the group of PET-CT (1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15) as compared to scintigraphy (1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24) patients. The increase in fold change was significant (p\u0026thinsp;=\u0026thinsp;0.001) and of large size (d\u0026thinsp;=\u0026thinsp;1.92) in PET-CT patients but not significant (p\u0026thinsp;=\u0026thinsp;0.31) and small (d\u0026thinsp;=\u0026thinsp;0.43) for scintigraphy patients. The difference in gene fold change between both patient groups was not significant (p\u0026thinsp;=\u0026thinsp;0.37) but medium (d\u0026thinsp;=\u0026thinsp;0.65). Individual gene expression results are shown in Supplemental Fig.\u0026nbsp;1 for PET-CT and in Supplemental Fig.\u0026nbsp;2 for scintigraphy patients.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD shows results of the γH2AX test. In contrast to gene expression, a higher level of response was detected in scintigraphy then in PET-CT patients: the fold change in the former patient group was significant (p\u0026thinsp;=\u0026thinsp;0.014) and of large size (d\u0026thinsp;=\u0026thinsp;1.00), while in the latter group it was not significant (p\u0026thinsp;=\u0026thinsp;0.33) and of medium size (d\u0026thinsp;=\u0026thinsp;0.708). Individual γH2AX test results for both patient groups are shown in Supplemental Fig.\u0026nbsp;3. The results of ROS analyses are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE. Except for 3 PET-CT patients, ROS fold changes in samples not additionally exposed to UVA were not distinguishable from 1 meaning that the diagnostic radiation exposure did not induce detectable oxidative stress. Additional exposure of 2 h samples to UVA radiation did not induce significant ROS level changes relative to 0 h samples exposed to UVA in any of the groups, but a large effect, with a downregulation pattern, was seen in PET-CT patients (p\u0026thinsp;=\u0026thinsp;0.82, d\u0026thinsp;=\u0026thinsp;0.95), not in scintigraphy patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.99, d\u0026thinsp;=\u0026thinsp;0.01) with the exception of one patient with an 8-fold decrease. This indicated that the diagnostic exposure of patients did not change the response of PBMC to additional oxidative stress induced by UVA irradiation. Complete numerical values of statistical tests are given in Supplemental Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to increase the statistical power of the analysis, fold changes from the two patient groups were pooled (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The result of gene expression and γH2AX was non-significant and of small size (except \u003cem\u003eBBC3\u003c/em\u003e and \u003cem\u003eXPC\u003c/em\u003e, for which the effect sizes were medium, and γH2AX, for which the effect size was large, see Supplemental Table\u0026nbsp;1 for p and d values). Highly scattered results were obtained for ROS without UVA with 2 donors showing fold changes of around 8. As expected, based on results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, additional UVA exposure did not lead to significant effects (p\u0026thinsp;=\u0026thinsp;0.8, d\u0026thinsp;=\u0026thinsp;0.16). Moreover, with the aim of seeing whether the combination of fold changes from all endpoints improved the power to detect radiation exposure, fold changes were pooled separately for PET-CT and scintigraphy patients and for both groups of patients together. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG. Interestingly, pooling of fold changes per patient group resulted in a somewhat higher and more spread-out values in the PET-CT as compared to scintigraphy cohort. Finally, pooling of all patients resulted in a significant but small mean fold change, with 1 out of 34 patients showing a combined fold change above 2, and 9 \u0026ndash; above 1.3. A twofold change relative to control is considered a conservative threshold which controls for false positive results (Riecke et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), but a fold change threshold of 1.3 is also considered biologically relevant (Jain and Das \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of gene expression, γH2AX foci and ROS changes with patient characteristics\u003c/h2\u003e \u003cp\u003eFold changes observed for the pool of patients for each of the endpoints were correlated to injected activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), calculated effective dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), blood activity left in blood at 2 hours post-procedure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and percent of injected activity left at 2 hours (Supplemental Fig.\u0026nbsp;5). In these figures, panels A-K show the corresponding correlations for: \u003cem\u003eBBC3\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e, \u003cem\u003eMDM2\u003c/em\u003e, \u003cem\u003eXPC\u003c/em\u003e, γH2AX, ROS (after PET or scintigraphy only or after additional UVA exposure), pool of genes and pool of endpoints, respectively. Additional correlation analyses were performed to determine how the different fold changes correlated between endpoints (Supplemental Fig.\u0026nbsp;6). Numerical values regarding linear regressions of these correlations, including equations, 95% confidence intervals, R\u003csup\u003e2\u003c/sup\u003e and Pearson r values are provided in Supplemental Tables\u0026nbsp;2\u0026ndash;6. Overall, there was a lack of steep slopes with low R\u003csup\u003e2\u003c/sup\u003e and r values.\u003c/p\u003e \u003cp\u003eγH2AX fold change presented a weak positive correlation with injected activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG and Supplemental Table\u0026nbsp;2). Conversely, ROS fold change (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH) as well as the fold change for pool of all endpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK) seemed to correlate inversely with injected activity, yet interpretations should be cautious given the scatter of data. There was a weak trend of positive correlation of effective dose with gene expression, ROS, and the pool of endpoints fold changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplemental Table\u0026nbsp;3). Correlation patterns with activity left in blood at 2 hours post-procedure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) or percent of injected activity left at 2 hours (Supplemental Fig.\u0026nbsp;5) were weak, and in some cases, driven by few individuals such as the apparent negative correlation observed for ROS with activity left in blood at 2 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH, Supplemental Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eSupplemental Fig.\u0026nbsp;6 show correlation analyses of fold changes in γH2AX \u003cem\u003evs.\u003c/em\u003e either gene expression or ROS, as well as correlations of fold changes in ROS \u003cem\u003evs.\u003c/em\u003e either gene expression or ROS plus additional UVA exposure. Numerical values for these correlations are provided in Supplemental Table\u0026nbsp;6. Results indicated that γH2AX correlated positively with gene expression (Supplemental Fig.\u0026nbsp;6A-F) and with a weak pattern of negative correlation with ROS fold changes (Supplemental Fig.\u0026nbsp;6M). Furthermore, there were no clear correlations between ROS and gene expression fold changes, with, maybe, the exception of \u003cem\u003eCDKN1A\u003c/em\u003e (Supplemental Fig.\u0026nbsp;6H), yet with a weak positive correlation. Also, there was no clear correlation between the ROS levels after diagnostic procedure and additional UVA exposure-induced ROS in 2 h samples (Supplemental Fig.\u0026nbsp;6N).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analyses were performed to assess the correlation of the observed fold changes with age (Supplemental Fig.\u0026nbsp;7), body mass (Supplemental Fig.\u0026nbsp;8), sex (Supplemental Fig.\u0026nbsp;9), and previous radiotherapy (Supplemental Fig.\u0026nbsp;10) or chemotherapy treatment (Supplemental Fig.\u0026nbsp;11). The numerical results are provided in Supplemental Tables\u0026nbsp;7\u0026ndash;11 and demonstrate weak correlations with the observed fold changes. However, the advanced age of a large proportion of patients, the limited number of females included in the study, and few patients with record of previous radiotherapy or chemotherapy regimen make the interpretation of these results difficult.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe potential of gene expression, γH2AX foci and ROS as biomarkers of low dose exposure in the absence of a control\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBecause in the event of a radiological emergency control samples are rarely available, and consequently, it is not possible to normalise data to an unexposed biological material, the response of each endpoint was further assessed based on raw data without normalisation to individual samples before the diagnostic procedure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These were: 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values for gene expression, median γH2AX and median ROS values. To compare results at 2 h vs. 0 h, one-way ANOVA with Šid\u0026aacute;k correction for multiple comparisons was used for gene expression and ROS since, for these endpoints, more than two factors were analysed (Supplemental Table\u0026nbsp;12). For γH2AX, paired t-test was used. The 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values of blood samples at 2 h did not differ statistically from those at 0 h for either PET or scintigraphy patients, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, respectively, and Supplemental Table\u0026nbsp;12. To increase the statistical power by considering all patients, which could be used to identify cohorts of people exposed to low dose radiation in a radiological emergency, PET-CT and scintigraphy patients were pooled. Gene expression at 2 h samples was compared to that at 0 h based on raw 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values for all patients pooled (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). ANOVA results, provided in Supplemental Table\u0026nbsp;12, indicated that the expression of none of the genes was significantly different from control samples at 2 h. Moreover, 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values of the pool of genes was not statistically different between these two timepoints based on paired t-test (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The median γH2AX intensity differed significantly at 0- and 2 h in scintigraphy patients alone (p\u0026thinsp;=\u0026thinsp;0.02, 0.42), Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, as well as the pool of all scintigraphy and PET patients (p\u0026thinsp;=\u0026thinsp;0.004, 0.36), Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, but not in PET patients alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). ROS levels were only significantly different from control values in samples exposed to UVA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH and I). Both group of patients, separately or pooled (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ), showed different ROS levels in UVA exposed samples at 0 h as compared to 0 h controls, 2 h samples as compared to 0 h controls, and 2 h samples as compared to 2 h controls, Supplemental Table\u0026nbsp;12. Importantly, when endpoints were pooled either for PET patients only (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK), scintigraphy patients only (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eL) or the pool of all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM), exposed samples could not be discriminated from unexposed samples, Supplemental Table\u0026nbsp;12.\u003c/p\u003e \u003cp\u003eCorrelation analyses were then performed with raw data at 0- or 2 h as justified below. Raw data at 0 h was correlated with patients\u0026rsquo; age (Supplemental Fig.\u0026nbsp;12), body mass (Supplemental Fig.\u0026nbsp;13) and sex (Supplemental Fig.\u0026nbsp;14), as these should, ideally, not determine the response of the analysed endpoints. Consequently, absence of correlation would be desirable as it would indicate high inclusiveness value of the corresponding biomarker. Also, raw data at 0 h would most likely not correlate with previous record of RT (Supplemental Fig.\u0026nbsp;15) or CHT (Supplemental Fig.\u0026nbsp;16) provided that these did not occur shortly before. No clear correlations were observed (Supplemental Fig.\u0026nbsp;12\u0026ndash;16) with the exception of a weak inverse correlation of ROS levels after UVA exposure with age (Supplemental Fig.\u0026nbsp;12I). We further tested how the different endpoints correlated based on raw data at 0 h as these could indicate the activity level of a potential background DNA damage response (Supplemental Fig.\u0026nbsp;17). With the exception of a weak positive correlation of \u003cem\u003eCDKN1A\u003c/em\u003e with γH2AX (Supplemental Fig.\u0026nbsp;17B), no clear correlations were identified in these analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA strong correlation of raw data at 2 h with injected activity (Supplemental Fig.\u0026nbsp;18), effective dose (Supplemental Fig.\u0026nbsp;19), blood activity remaining at 2 h (Supplemental Fig.\u0026nbsp;20) and the percent of injected activity remaining in blood at 2 h (Supplemental Fig.\u0026nbsp;21) would indicate the potential of these biomarkers to discriminate individuals exposed to low doses, even in the absence of an unexposed control. These results indicated, however, only a weak pattern of increased 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values, i.e. higher expression, with higher effective doses for \u003cem\u003eCDKN1A\u003c/em\u003e (Supplemental Fig.\u0026nbsp;19B) and \u003cem\u003eXPC\u003c/em\u003e (Supplemental Fig.\u0026nbsp;19F), but with large data scatter. Driven by a few individuals, a weak positive correlation was found for ROS levels with effective dose (Supplemental Fig.\u0026nbsp;19H) and for \u003cem\u003eBBC3\u003c/em\u003e expression with activity in blood at 2 h (Supplemental Fig.\u0026nbsp;20A). Finally, correlation analyses between endpoints for raw data at 2 h (Supplemental Fig.\u0026nbsp;22), which would ideally represent the coordinated induction of the DNA damage response at different levels after exposure to these low doses, resulted in generally flat slopes, and low R\u003csup\u003e2\u003c/sup\u003e and r values, with the exception of a positive slope observed for \u003cem\u003eCDKN1A vs.\u003c/em\u003e γH2AX (Supplemental Fig.\u0026nbsp;22B).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDNA damage, ROS levels and gene expression changes induced by PET-CT or scintigraphy exposure were determined using blood collected before and after the diagnostic procedures and correlated to patients\u0026rsquo; data to further characterize these endpoints as biomarkers of IR exposure. Although correlation analyses revealed generally mild slopes and low r values, γH2AX fold change presented a weak positive correlation with injected activity, indicating that exposure from these diagnostic procedures induced a subtle increase in DNA damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To note, each individual was sampled before and after procedure, acting as his/her own control, being a strength of this study. Consistent with the overall weak fold changes at the level of γH2AX, as well as ROS, the expression changes in the panel of radiation-responsive genes tested at 2 h post-procedure were also generally low relative to control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Some patients did show consistent upregulation of several endpoints, e.g. patient P-12, S-06 and P-05, while others showed downregulation, e.g. patients P-06 and P-02.\u003c/p\u003e \u003cp\u003eThe reason for variability in the response between patients who received comparable injected activities or effective doses remains unclear. P-12 and S-06, who were among those with highest γH2AX levels (DNA damage data were not available for P-05) showed, together with P-05, the highest gene expression upregulation. The effective doses received by P-12, S-06 and P-05 were in the high range. S-06, whose effective dose was lower than P-12 or P-05, had, nevertheless, received a high injected activity and showed a high remaining activity at 2 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, P-06 and P-02 showed gene expression downregulation and reduced ROS levels (if available) relative to control, while their effective doses were also high, yet had intermediate injected activity in relative terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For internal exposures, the biokinetics and radionuclide decay have a strong impact on gene expression in white blood cells, which indeed correlates best with the kinetics of dose decay (exponential decay of activity) rather than with total absorbed dose (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This, however, did not seem to explain the observed differences between P-12, S-06 and P-05 with P-06 and P-02 patients considering the comparable amount of activity lost during the 2 h gap since administration.\u003c/p\u003e \u003cp\u003eThe different responses observed in patient samples could also be related to diverse pathophysiological stages (Whitney et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), different individual radiosensitivity (Badie et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and/or variable activation of the DDR following low doses (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A panel of genes predictive for radiation toxicity has been previously described (Rieger et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), although it does not include the genes analysed here. P-05 had previously undergone radiotherapy, but information regarding tissue reaction was not available. Up- or downregulation patterns have been observed earlier for different individuals undergoing interventional imaging procedures for the \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e and \u003cem\u003eMDM2\u003c/em\u003e genes (Visweswaran et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and in patients undergoing SPECT myocardial perfusion imaging (MPI) for the \u003cem\u003eMDM2\u003c/em\u003e and \u003cem\u003eBBC3\u003c/em\u003e genes (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, the relative induction of \u003cem\u003eCDKN1A\u003c/em\u003e and \u003cem\u003eGADD45A\u003c/em\u003e ranged between ca 2- to- 7-fold and between 1- to 7- fold, respectively, in patients diagnosed with different malignancies 6 h after the first 1.5 Gy fraction of TBI (Amundson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Some degree of variation in \u003cem\u003eFDXR\u003c/em\u003e expression was observed in healthy human donors even at 0 h (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and in lymphoblastoid cells from different individuals 12 h post 10 Gy exposure, for \u003cem\u003eCDKN1\u003c/em\u003e and \u003cem\u003eGADD45A\u003c/em\u003e in addition to \u003cem\u003eFDXR\u003c/em\u003e (Jen and Cheung \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It seems that there is a lower interindividual variation for \u003cem\u003eFDXR\u003c/em\u003e, which is expressed at a low level endogenously (Manning et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), than for \u003cem\u003eCDKN1A\u003c/em\u003e (Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, large interindividual variability in some \u003cem\u003eFDXR\u003c/em\u003e variants appear in response to TBI exposure, with CVs between 19.6\u0026ndash;46 depending on the variant (Cruz-Garcia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although not within the scope of this study, it would be interesting to monitor these patients for tissue response provided upcoming radiotherapy treatment. Also, to conduct genetic analyses, as polymorphisms in the \u003cem\u003etrans\u003c/em\u003e regulators of radiation-induced expression genes are major phenotype-determinants, only\u0026thinsp;\u0026lt;\u0026thinsp;1% controlled by \u003cem\u003ecis\u003c/em\u003e-acting regulators (Smirnov et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo statistically compare the changes observed at 2 h to those at 0 h, PET and scintigraphy patients were pooled, first per group, and then altogether. This approach was justified not only from the perspective of gaining statistical power, but importantly, because radiological emergencies may involve a wide range of doses, radiation qualities, and individuals with unique profiles. IR exposure biomarkers should, ideally, discriminate exposed and unexposed individuals in a heterogenous population independently, or with a moderate- to- low impact, of disease or infection status (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), prior exposure to chemotherapy (Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), sex (Cruz-Garcia et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cruz-Garcia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and anti-oxidant levels (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Age and sex contribute, for example, less than 20\u0026ndash;30% to the total inter-individual variance in gene expression, which is considered negligible, as their impact on fold changes still fall within the two-fold equivalent to control values (Agbenyegah et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A non-significant trend of increased H2AX, p53 and ATM phosphorylation was observed in lymphocytes from 20\u0026ndash;25-year-old individuals compared to a 40\u0026ndash;55 age group following a 25 mGy dose (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our study, correlations of γH2AX, gene expression, and ROS fold changes with age (Supplemental Fig.\u0026nbsp;7) were unclear, partly due to the bias towards older individuals. Moreover, no clear correlations were observed between raw data at 0 h and body mass (Supplemental Fig.\u0026nbsp;13), sex (Supplemental Fig.\u0026nbsp;14), previous record of RT (Supplemental Fig.\u0026nbsp;15) or CHT (Supplemental Fig.\u0026nbsp;16) indicating a high inclusiveness value of the studied biomarkers, albeit with the limitation of small sample size for some variables. Besides, with the exception of a weak positive correlation of \u003cem\u003eCDKN1A\u003c/em\u003e with γH2AX at 0 h (Supplemental Fig.\u0026nbsp;17B), no clear correlations between endpoints were identified at 0 h which would suggest an active DNA damage response in these patients before the diagnostic examinations (Supplemental Fig.\u0026nbsp;17).\u003c/p\u003e \u003cp\u003eCoherent with the higher activity administered to the scintigraphy patients (mean injected activity of 731\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5 MBq) than to the PET-CT patients (277\u0026thinsp;\u0026plusmn;\u0026thinsp;61.7 MBq), and the twice as high mean activity left in blood at 2 h in the scintigraphy patients, scintigraphy induced a statistically significant upregulation of γH2AX levels as compared to control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This was not detected for PET-CT, despite a medium effect size. The statistical significance of γH2AX fold change at 2 h was lost when scintigraphy and PET groups were pooled (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). However, the induction of γH2AX still correlated positively with gene expression fold changes (Supplemental Fig.\u0026nbsp;6A-F). In line with this finding, up-regulation of γH2AX (Halm et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rothkamm et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vandevoorde et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and \u003cem\u003eFDXR\u003c/em\u003e expression (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) were detected after low computed tomography doses. Also, those SPECT MPI patients with increased γH2AX after procedure showed significant upregulation of DDR-related genes such as \u003cem\u003eTp53\u003c/em\u003e and \u003cem\u003eMDM2\u003c/em\u003e (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Using blood samples exposed \u003cem\u003eex vivo\u003c/em\u003e with a CT scanner, both radiation-induced foci (RIF) and \u003cem\u003eFDXR\u003c/em\u003e expression increased linearly with dose at comparable unit rates, with a significant increment relative to control at 11.3 mGy up to 49.7 mGy for a 3-fold RIF, and at 22.6 mGy up to 49.7 mGy for a 4-fold \u003cem\u003eFDXR\u003c/em\u003e expression (Schule et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsistent with a generally low level of induced DNA damage, only weak trends of gene expression upregulation relative to control were observed. These included \u003cem\u003eBBC3\u003c/em\u003e, \u003cem\u003eXPC\u003c/em\u003e and \u003cem\u003eGADD45A\u003c/em\u003e genes in both groups, and \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eFDXR\u003c/em\u003e and \u003cem\u003eMDM2\u003c/em\u003e in PET-CT patients only (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). \u003cem\u003eBBC3\u003c/em\u003e showed medium effect size (with large p-value) in scintigraphy and PET patients, which also applied to \u003cem\u003eFDXR\u003c/em\u003e and \u003cem\u003eXPC\u003c/em\u003e in PET patients only. \u003cem\u003eFDXR\u003c/em\u003e is one of the most IR-responsive genes in PBMC (Cheng et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manning et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and among those with the best dose discrimination power (Lacombe et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eBBC3, XPC\u003c/em\u003e and \u003cem\u003eCDKN1A\u003c/em\u003e are also identified as top predictor genes of radiation response in humans (Dressman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The upregulation of these genes could indicate: an initial cell cycle arrest through \u003cem\u003eCDKN1A\u003c/em\u003e (Brugarolas et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and \u003cem\u003eGADD45A\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (albeit PBMC are not dividing); a pro-apoptotic response in heavily damaged cells, led by \u003cem\u003eBBC3\u003c/em\u003e (Chipuk et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jeffers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and \u003cem\u003eFDXR\u003c/em\u003e (Hwang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Liu and Chen \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the activation of DNA damage repair through \u003cem\u003eXPC\u003c/em\u003e (Adimoolam and Ford \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sugasawa et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For both groups together, \u003cem\u003eBBC3\u003c/em\u003e and \u003cem\u003eXPC\u003c/em\u003e expression at 2 h remained with a medium effect size (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eThe weak gene expression changes observed on average here are in agreement with previously reported small changes in \u003cem\u003eFDXR\u003c/em\u003e expression (1.3-1.7-fold change) in 6 out of 8 patients at 2 h after CT exposure with estimated doses to the blood of 3.9\u0026ndash;20.9 mGy (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eCDKN1A\u003c/em\u003e showed a ca 27-fold increase while \u003cem\u003eGADD45A\u003c/em\u003e remained close to control levels 6 h after 1.5 Gy delivered by TBI treatment in a non-Hodgkin\u0026rsquo;s lymphoma patient (Amundson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). \u003cem\u003eCDKN1A\u003c/em\u003e and \u003cem\u003eGADD45A\u003c/em\u003e showed a\u0026thinsp;\u0026le;\u0026thinsp;2-fold upregulation relative to control 2 h after CT scan with estimated doses of 10\u0026ndash;43 mGy and after 6 mGy \u003csup\u003e18\u003c/sup\u003eF-FDG injection followed by a 0.2 cGy (2 mGy) CT scan (Riecke et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eFDXR\u003c/em\u003e was, however, significantly upregulated 24 h after TBI, and continuously during fractionated treatment for several malignancies (O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following the first fraction with a 0.038\u0026ndash;0.169 Gy dose to the blood, the mean fold changes in six radiotherapy patients are: 1.45, for \u003cem\u003eFDXR\u003c/em\u003e; 1.67, for \u003cem\u003eCDKN1A\u003c/em\u003e; 1.08, for \u003cem\u003eBBC3\u003c/em\u003e; 1.09, for \u003cem\u003eMDM2\u003c/em\u003e; and 1.02, for \u003cem\u003eGADD45\u003c/em\u003e (Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When considering the overall gene expression response by the pool of genes, PET patients showed a statistically significant upregulation, but not scintigraphy patients, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. This observation was interesting in light of the already discussed higher activity injected in the scintigraphy group and might reflect dose-dependent kinetics of transcription.\u003c/p\u003e \u003cp\u003eConsidering the dynamic nature of γH2AX foci (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and of gene expression changes (Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabacik et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Macaeva et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), it would have been interesting to study additional time points after exposure. This was, unfortunately, not possible due to sampling constraints for practical reasons in the clinic. It is plausible to assume that a different magnitude of response could have been detected had other timepoints (and/or gene-sets) been chosen. Nevertheless, the available timepoint was justified based on \u003cem\u003ein vivo\u003c/em\u003e gene expression changes detected already at 2 h after low doses (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; O'Brien et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and, as already discussed, the fold changes are in the range observed by others early after low doses to the blood and correlate with the low level of damage induced. γH2AX foci have been shown to appear 5 minutes after low dose exposure, peak at 15\u0026ndash;30 minutes and decrease to baseline levels by 120 minutes (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eFDXR\u003c/em\u003e has been shown to peak at 8 h, with a mean fold change of 2.7 across radiotherapy patients, and becomes significantly upregulated relative to control at 24 h with a mean fold change of 1.8 after the first fraction (Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, \u003cem\u003eCDKN1A\u003c/em\u003e was shown to increase significantly at 24 h post first radiotherapy fraction, with a mean fold change of 1.6, while neither \u003cem\u003eBBC3\u003c/em\u003e, \u003cem\u003eMDM2\u003c/em\u003e or \u003cem\u003eGADD45A\u003c/em\u003e were significantly upregulated over a 24 h time period post low doses to the blood (Cruz-Garcia et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e, \u003cem\u003eXPC\u003c/em\u003e, and \u003cem\u003eMDM2\u003c/em\u003e were also significantly upregulated (maximum fold change of ca 8.5 for \u003cem\u003eFDXR\u003c/em\u003e) at 72 h post-\u003csup\u003e131\u003c/sup\u003eI- metaiodobenzylguanidine (\u003csup\u003e131\u003c/sup\u003eI- mIBG) exposure (at doses in the range 2\u0026ndash;3 Gy and calculated doses to the blood of 0.45\u0026ndash;1.97 Gy) in neuroblastoma paediatric patients as shown in two related studies (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These genes were also upregulated (6-fold upregulation for \u003cem\u003eFDXR\u003c/em\u003e) in \u003csup\u003e131\u003c/sup\u003eI- mIBG-irradiated patients at 96 h post-exposure, although with a general pattern of downregulation as compared to the 72 h timepoint (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At 15 days after exposure, the fold changes of \u003cem\u003eCDKN1A\u003c/em\u003e and \u003cem\u003eGADD45A\u003c/em\u003e were not different from control, while \u003cem\u003eMDM2\u003c/em\u003e and \u003cem\u003eXPC\u003c/em\u003e became downregulated (ca 0.7-fold), and \u003cem\u003eFDXR\u003c/em\u003e remained upregulated (ca 1.5-fold), indicating that the DDR may be active long after exposure (Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn agreement with gene expression results, only the PET group showed a medium effect size for ROS upregulation, which was not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Moreover, additional exposure of samples to UVA radiation did not induce a significant increase of ROS in scintigraphy patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.99, d\u0026thinsp;=\u0026thinsp;0.01) nor in PET-CT patients, who showed, nevertheless a large effect size with a downregulation pattern (p\u0026thinsp;=\u0026thinsp;0.82, d\u0026thinsp;=\u0026thinsp;0.95). This suggests that the diagnostic exposure of patients did not change the impact of UVA irradiation in PBMC. It should be noted, however, that low doses of radiation were reported to induce oxidative stress leading to oxidised nucleotides in the cellular cytoplasm (Haghdoost et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sangsuwan and Haghdoost \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A weak trend of positive correlation of ROS fold change with effective dose was found (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), but correlations of this endpoint with injected activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), activity remaining in blood at 2 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH) or percent of injected activity remaining at 2 h (Supplemental Fig.\u0026nbsp;5H) were weak or unclear due to large data scatter. That ROS fold changes, gene expression and the pool of endpoints presented a weak but positive correlation with effective dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), manifested the relevance of considering isotope-specific conversion factors to account for different biodistributions, despite the fact that beta-emitters deposit most of their emitted energy locally, i.e. within the blood, tumour or target organs (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA secondary, yet relevant aspect of our study was the assessment of gene expression, γH2AX foci and ROS as biomarkers of \u003cem\u003ein vivo\u003c/em\u003e low dose exposure even in the absence of a control sample, such as following a radiological emergency. In such situations, it has been suggested that cycle threshold (ΔCT) values may serve as exposure indicators provided that RNA amount and quality input are precisely controlled (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This strategy has been successfully applied \u003cem\u003eex vivo\u003c/em\u003e (Brzoska and Kruszewski \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Paul and Amundson \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and \u003cem\u003ein vivo\u003c/em\u003e (Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Non-irradiated and irradiated samples in the range of 1.25 Gy (one fraction) to 3.75 Gy (delivered in three fractions) were discriminated with high accuracy in patients receiving total body irradiation (Dressman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Filiano et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lucas et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meadows et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, blood samples of prostate cancer patients receiving intensity-modulated radiation therapy (IMRT) were discriminated from preexposure control samples based on \u003cem\u003eFDXR\u003c/em\u003e expression at 24 h after equivalent blood doses as low as 0.09\u0026thinsp;\u0026minus;\u0026thinsp;0.017 Gy (Abend et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Exposed and unexposed samples were also discriminated after internal exposures using a panel of genes including \u003cem\u003eFDXR\u003c/em\u003e and \u003cem\u003eCDKN1A\u003c/em\u003e (Edmondson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Evans et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The aim here was to test whether 0- and 2 h samples could be discriminated based on raw data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExposed samples were not significantly different from unexposed samples when using the pool of all endpoints for PET (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK) and scintigraphy patients alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eL) or all patients pooled (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM). ROS levels only deviated from control values in UVA-exposed samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH and I) and none of the individual genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), nor the pool of genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), had a significantly different expression from control samples at 2 h when considering all patients pooled. However, the median γH2AX intensity at 2 h differed significantly from that at 0 h in scintigraphy patients alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF) and the pool of all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). γH2AX foci analysed by immunofluorescence microscopy revealed a significantly enhanced frequency in PBMC after low doses of X-radiation delivered during neuro-interventional procedures (Visweswaran et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), not significant for the increase in γH2AX mean fluorescence intensity in post-diagnostic (observed in 64.5% of patients) and post-therapeutic (50% of patients) neuro-interventional procedures as compared to pre-exposure controls (Visweswaran et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, not significant when analysing the percentage of γH2AX positive cells at 30 min post-SPECT as compared to baseline levels (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The γH2AX relative fluorescence intensity was found to correlate poorly with the entrance surface dose values, i.e. absorbed dose by the skin in a particular region or organ, measured with a thermoluminescence dosimeter in patients undergoing neuro-interventional diagnostic (P\u0026thinsp;=\u0026thinsp;0.199, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0563) and therapeutic (P\u0026thinsp;=\u0026thinsp;0.617, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.015) procedures from 9- to-225 mGy (Visweswaran et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Besides, the expression of \u003cem\u003eCDKN1A\u003c/em\u003e (0.55-fold change), \u003cem\u003eMDM2\u003c/em\u003e (0.57-fold) and \u003cem\u003eFDXR\u003c/em\u003e (0.84-fold), and \u003cem\u003eGADD45A\u003c/em\u003e (1.1-fold) did not differ statistically from control samples 24 h after low doses of neuro-interventional radiological procedures (Visweswaran et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In line with this, correlation analyses between the tested endpoints for raw data at 2 h (Supplemental Fig.\u0026nbsp;22) did not reveal a clear induction of the DNA damage response after exposure to the tested low doses when considering raw data without normalisation.\u003c/p\u003e \u003cp\u003eThe shape of the dose response curve for cellular effects after low doses and low dose rates is largely uncertain. While the radiation protection system is quantitatively valuable, implicit assumptions in risk estimation associated to low doses and protracted IR exposures would benefit from stronger evidence through further experimental data (Kreuzer et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shore et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Biomarkers of IR exposure help to understand the molecular and cytogenetic effects of low doses, to be considered in epidemiological studies (Hall et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pernot et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or biodosimetry applications (Swartz et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This demands, however, appropriate validation of biomarkers by using biological samples exposed \u003cem\u003ein vivo\u003c/em\u003e, whose availability is usually limited for obvious reasons. We further characterized γH2AX, ROS levels and transcriptomic changes as biomarkers of IR exposure \u003cem\u003ein vivo\u003c/em\u003e using blood from patients undergoing PET-CT and skeletal scintigraphy. γH2AX fold changes correlated weakly, but positively, with injected activity, indicating a radiation-induced increase in DNA damage with dose. γH2AX upregulation also correlated positively with mild changes in transcription of known radiation-responsive genes, suggesting a coherent activation of the DDR pathway following diagnostic imaging-induced genotoxic stress. For reasons to be determined, some patients showed consistent stronger up- or down-regulation of several endpoints after comparable injected activities or effective doses, which could relate to differences in radiosensitivity and/or DDR activation after low doses (Lee et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This cohort included 29 males and 5 females, with ages ranging from 41 to 80 and an average age of 66. Given the relatively small population, with a bias towards older and male individuals, it would be highly desirable to expand this study by increasing the number of patients, and, if possible, conducting genetic and radiosensitivity analyses as well as tissue response monitoring, if applicable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was partly supported by the Swedish Radiation Safety Authority SSM, grant number SSM2017-112.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Regional Medical Chamber, Kielce, Poland (8\u003csup\u003eth\u003c/sup\u003e December 2015/No. 16-D/2015).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbend M, Amundson SA, Badie C, Brzoska K, Hargitai R, Kriehuber R, Schule S, Kis E, Ghandhi SA, Lumniczky K, Morton SR, O'Brien G, Oskamp D, Ostheim P, Siebenwirth C, Shuryak I, Szatmari T, Unverricht-Yeboah M, Ainsbury E, Bassinet C, Kulka U, Oestreicher U, Ristic Y, Trompier F, Wojcik A, Waldner L, Port M (2021) Inter-laboratory comparison of gene expression biodosimetry for protracted radiation exposures as part of the RENEB and EURADOS WG10 2019 exercise. 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Radiat Oncol 6:62. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1748-717X-6-62\u003c/span\u003e\u003cspan address=\"10.1186/1748-717X-6-62\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"radiation-and-environmental-biophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rebs","sideBox":"Learn more about [Radiation and Environmental Biophysics](http://link.springer.com/journal/411)","snPcode":"411","submissionUrl":"https://submission.nature.com/new-submission/411/3","title":"Radiation and Environmental Biophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gene expression, γH2AX foci, ROS, lymphocytes, blood, diagnostic imaging patients.","lastPublishedDoi":"10.21203/rs.3.rs-2873007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2873007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCandidate ionising radiation exposure biomarkers must be validated in humans exposed \u003cem\u003ein vivo\u003c/em\u003e. Blood from patients undergoing positron emission tomography - computed tomography scan (PET-CT) and skeletal scintigraphy (scintigraphy) was drawn before (0 h) and after (2 h) the procedure for correlation analyses of response of selected biomarkers with radiation dose and other available patient information. \u003cem\u003eFDXR\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eBBC3\u003c/em\u003e, \u003cem\u003eGADD45A\u003c/em\u003e, \u003cem\u003eXPC\u003c/em\u003eand \u003cem\u003eMDM2\u003c/em\u003e expression was determined by qRT-PCR, DNA damage (γH2AX) by flow cytometry, and reactive oxygen species (ROS) levels by flow cytometry using the 2', 7' – Dichlorofluorescin diacetate test in peripheral blood mononuclear cells (PBMC). For ROS experiments, 0- and 2 h samples were additionally exposed to UVA to determine whether diagnostic irradiation conditioned the response to further oxidative insult.\u003c/p\u003e\n\u003cp\u003eWith some exceptions, radiological imaging induced weak γH2AX foci, ROS and gene expression fold changes, the latter with good coherence across genes within a patient. Diagnostic imaging did not influence oxidative stress in PBMC successively exposed to UVA. Correlation analyses with patient characteristics led to low correlation coefficient values. γH2AX fold change, which correlated positively with gene expression, presented a weak positive correlation with injected activity, indicating a radiation-induced subtle increase in DNA damage and subsequent activation of the DNA damage response pathway. The exposure discrimination potential of these biomarkers in the absence of control samples, as frequently demanded in radiological emergencies, was assessed using raw data. These results suggest that the variability of the response in heterogeneous populations might complicate identifying individuals exposed to low radiation doses.\u003c/p\u003e","manuscriptTitle":"The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-05 15:12:57","doi":"10.21203/rs.3.rs-2873007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-15T16:37:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-12T19:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"02e0c7db-5bad-42d9-987b-a75b4313cc2a","date":"2023-05-04T05:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-04T04:33:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-04T04:29:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-05-03T02:18:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Radiation and Environmental Biophysics","date":"2023-04-28T12:38:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"radiation-and-environmental-biophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rebs","sideBox":"Learn more about [Radiation and Environmental Biophysics](http://link.springer.com/journal/411)","snPcode":"411","submissionUrl":"https://submission.nature.com/new-submission/411/3","title":"Radiation and Environmental Biophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b4d3212f-0852-4402-988e-746dcd224372","owner":[],"postedDate":"May 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:53:51+00:00","versionOfRecord":{"articleIdentity":"rs-2873007","link":"https://doi.org/10.1007/s00411-023-01033-4","journal":{"identity":"radiation-and-environmental-biophysics","isVorOnly":false,"title":"Radiation and Environmental Biophysics"},"publishedOn":"2023-06-19 21:19:02","publishedOnDateReadable":"June 19th, 2023"},"versionCreatedAt":"2023-05-05 15:12:57","video":"","vorDoi":"10.1007/s00411-023-01033-4","vorDoiUrl":"https://doi.org/10.1007/s00411-023-01033-4","workflowStages":[]},"version":"v1","identity":"rs-2873007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2873007","identity":"rs-2873007","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}